Validating biomarker measurement

ABSTRACT

A method for validating quantification of biomarkers, the biomarkers being quantified using a quantification technique of a selected type, and the method including determining a plurality of biomarker values, each biomarker value being indicative of a value measured or derived from a measured value, for at least one corresponding biomarker of the biological subject and being at least partially indicative of a concentration of the biomarker in a sample taken from the subject, determining at least one control value by determining a combination of biomarker values, comparing each control value to a respective control reference and determining if the biomarker values are valid using results of the comparison.

BACKGROUND OF THE INVENTION

The present invention relates to a method and apparatus for validatingmeasurement of biomarker values used in generating an indicator, and inone example, to a method and apparatus for validating an indicator usedin determining the likelihood of a biological subject having at leastone medical condition.

DESCRIPTION OF THE PRIOR ART

The reference in this specification to any prior publication (orinformation derived from it), or to any matter which is known, is not,and should not be taken as an acknowledgment or admission or any form ofsuggestion that the prior publication (or information derived from it)or known matter forms part of the common general knowledge in the fieldof endeavour to which this specification relates.

Measurement of gene expression (as RNA or protein) in samples taken fromliving organisms has practical applications including, but not limitedto, determining a disease state, determining disease extent or severity,disease prognosis and early identification, identifying a tissue type(both normal and diseased including cancers), identifying andenumerating cell types in a cell mix, and understanding normal metabolicprocesses and their response to external factors or insults (includinginjury, wounds, burns, stress, viral or bacterial or parasitic or fungalinfection, exercise, diet, therapeutics, toxins, therapies, treatmentsand experimental procedures). There are a number of methods availablefor measuring gene expression (as RNA or protein) that are well known inthe art, from low-throughput (single genes and gene products) tohigh-throughput (exome and arrays), including northern blots, polymerasechain reaction (qPCR), microarrays, RNA sequencing (RNA-seq), targetedRNA sequencing, ELISA, EIA, mass spectrometry, HPLC, SNP analysis, andepigenetic technologies (ChIP-Seq, Chromatin Conformational Signatures(CCA), DNA methylation analyses).

Each of these technologies produces a value, or a set of values for eachof the products measured. In the context of medical discovery and itsapplications, these values are termed ‘biomarkers’. A measured value forany gene expression product (as RNA or protein) is defined as a measuredbiomarker, as measured by the processing instrument or device. Examplesof a measured biomarker include a protein concentration for a specifiedprotein, the transcript count for a single transcript in the case of RNAsequencing, the expression value for an exon or transcript in the caseof microarrays, an m/z value in the case of mass spectrometry or afluorescence value in the case of flow cytometry. Measured biomarkerscan be understood as ‘raw data’, as measured by the instrument.Multi-biomarker assays will measure a number of biomarkers in parallel,reporting a collection of measured biomarkers.

Indicator values are values that are designed to correlate, classify, orotherwise be indicative of some condition, stage, diagnosis or prognosisor absence thereof. For example temperature reported in degrees is anindicator value for fever. Arbitrarily complex indicators may be builtfor any purpose, and in the case of multi-biomarker medical devices, theindicator will be some combination of biomarkers that, through anequation, generate an indicator value that correlates to some state orcondition (or the absence of such) for a patient.

The development and use of indicator values requires accurate and validmeasurement of gene expression (as RNA or protein) measured values, andcan be achieved through the use of two key steps: normalisation andcontrols. Controls provide a check that the underlying values are valid,and normalisation is any method by which samples can be made comparableby removing non-biological sources of variation between samples.

Controls are used to ensure that relevant potential modes of failure canbe detected. If a failure is detected in a control, the assay orexperiment can be declared failed and the indicator value (if any) willconsequently also be invalid. In the context of medical devices,controls guard against the results of the test (indicator values) beingreported when the underlying inputs to the indicator may be invalid thusavoiding the potential of the operator drawing false conclusions.Controls that can be used include the following (which ones used dependsin part upon the user, the application and the stage of development ofthe assay):

-   -   No template control (run in parallel with other reactions to        determine the extent of contamination—for PCR)    -   No amplification control for assays relying on nucleic acid        amplification (e.g., contains no polymerase—this checks for        example the integrity of a labelled probe relying on the FRET        principle—for PCR)    -   Positive control (a sample used during assay use to confirm that        the test is able to produce a result in the reportable range).        This does not imply that the reported value for this control        will be above a given threshold (for example positive for a        disease), merely that the control is positively able to generate        a value in the reportable range of the test. Positive controls        may be biological in origin or synthetic (or a hybrid of the        two, such as recombinant products), and the positive controls        can be internal (coming from within a sample being tested) or        external (run in parallel and independent of the sample being        tested). Positive controls can include positive or negative        biological or synthetic controls.    -   A control containing no reverse transcriptase (to determine the        extent of contaminating DNA especially if primers are not        designed across an exon/intron border—for PCR)    -   Spike-in controls include artificial nucleic acid added to        either the sample to be tested (at any stage) and are used to        determine the extent of PCR inhibition and for quality control        in array and RNA-seq (for quantification, sensitivity, coverage        and linearity).

Of these measured controls perhaps the most common are external positivecontrols containing known concentrations of a given analyte, andspike-ins. The use of such controls contributes to the expense andcomplexity of running an experiment, or assay, through having topurchase reagents and the controls themselves, through the use ofexperimental “real estate” which could otherwise be used for targets,and in the additional resources and complexity inherent in having thesecontrol targets in addition to the targets required to produce theindicator value. It is therefore advantageous to reduce the additionalmeasured controls not measured in the course of determining the indictorvalue.

Normalisation is an important step that ensures that comparisons betweensamples, or between a reference and a sample can be made. The objectiveof the normalisation step is to remove differences not attributable tobiological variability, such as batch effect and other sources oftechnical variability including those introduced by concentration, time,temperature, instruments, operators or assay parameters (including thoseunknown or outside of the control of the assay users) such as thoseintroduced in a typical workflow, such as that described below.

Measurement of gene expression using microarrays or PCR or RNA-seq byexample usually involves some or all of the following steps depending onthe method (similar types of controls are required in most experimentsmeasuring biomarkers):

-   -   Experimental design including power calculation and number of        replicates to be used    -   Isolation of RNA or mRNA from sample(s) of interest    -   Determination of RNA quality and quantity    -   Fragmentation and size selection (for RNA-seq)    -   Conversion of RNA to complementary DNA (cDNA)    -   Conversion of cDNA to cRNA (for certain microarrays)    -   Fragmentation and labelling (for arrays, or use of a labelled        probe for PCR)    -   Detection    -   Data capture    -   Determination of data quality    -   Data normalisation    -   Control for false discovery.

Some of the experimental method variables that need to be controlled for(normalized) are detailed in Table 1 below, adapted from Roche AppliedScience Technical Note No. LC15/2002, under the appropriate step.

TABLE 1 Sample DNA/RNA cDNA Sample Nucleic acid Reverse ProductPreparation isolation transcription Amplification Detection PreparationIsolation Efficiency Efficiency Method used method method Enzyme EnzymeLinearity of Stability of Purity variability variability assay nucleicacid Variability Storage of isolation Storage

So that datasets can be compared, and that publicly available data is ofhigh quality, minimum information guidelines for gene expressionanalysis experiments have been published in scientific journals for bothPCR and microarrays (Bustin S A, Benes V, Garson J A, Hellemans J,Huggett J, et al. (2009) The MIQE Guidelines: Minimum Information forPublication of Quantitative Real-Time PCR Experiments. ClinicalChemistry 55: 611-622) (Brazma A, Hingamp P, Quackenbush J, Sherlock G,Spellman P, et al. (2001) Minimum information about a microarrayexperiment (MIAME)-toward standards for microarray data. Nat Genet 29:365-371) and are publicly available for RNA-seq(MINSEQE—www.mged.org/minseqe/).

Normalisation of data to account for these effects using measuredbiomarkers is common. For example, an external positive control at aknown concentration may be run in parallel with a sample. The value ofthe measured biomarker value in the sample can then be inferred(normalized) with reference to the measured external positive control.This is the concept behind a standard calibration curve used fornormalisation. Another common normalisation method using measuredbiomarkers uses internal positive controls; for example, in an RNAsequencing experiment, certain genes (or groups of genes) may be assumedto have a constant biological level of expression (these are thenormalizer biomarkers). Differences in the measured values for thesenormalizer biomarkers between samples is then assumed to benon-biological. The measured values for each sample are then adjusted upor down such that the normalizer biomarkers in each sample have the samevalue and the data is then said to be normalized. The normalized valuesof each biomarker may then be directly compared between samples, forexample for the diagnosis of a medical condition. Extensions of thisconcept are also known, for example Robust Microarray Analysis(Irizarry, R A; Hobbs, B; Collin, F; Beazer-Barclay, Y D; Antonellis, KJ; Scherf, U; Speed, T P (2003). “Exploration, normalisation, andsummaries of high density oligonucleotide array probe level data.”.Biostatistics 4 (2): 249-64) where the measured values for each sampleare adjusted such that the normalized values for each sample fit thesame distribution.

In practice, microarrays and RNA-seq and other platforms are often usedin the early “discovery” or research stage of experimentation togenerate sets of measured biomarkers covering the exome or genome orregulatory mechanisms thereof. The set of measured biomarkers generatedin such discovery experiments may be upwards of 6,000 genes ortranscripts, or up to 1,000,000 peaks in the case of tandem massspectrometry discovery datasets. There are typically many more measuredbiomarkers in each dataset than patient samples. This leads directly tofalse discovery problems as will be appreciated by someone skilled inthe art of biomarker discovery. A false discovery is when a measuredbiomarker with no genuine biological correlation to the condition underconsideration by chance happens to correlate to said condition. Thesefalse discoveries are indistinguishable from true discoveries until morepatient samples have been tested.

Once certain biomarkers have been “discovered”, or shown to besignificantly correlated to the desired experimental endpoint, a minimalset of biomarkers is often migrated to an appropriate clinical device,such as qPCR or Point-Of-Care RNA-sequencing platforms, along with aminimal set of appropriate controls.

qPCR currently has significant and commercially attractive advantagesover microarrays and RNA-seq (including targeted RNA-seq), especiallywhen used in a clinical environment. Such advantages include fastturnaround time, limited technician hands-on-time to set up an assay,limited technical skill level required to run an assay, accessibilityand availability of PCR machines, small footprint of PCR machines, easeof results interpretation, limited need for supporting informationtechnology infrastructure (software, algorithms, hardware, networks),limited license fees, availability and cost of reagents. Such factorslead to reduced cost of goods sold and a higher likelihood of marketacceptance of an assay.

The successful migration of relevant biomarkers to qPCR is currentlylimited by a number of factors including:

-   -   Limited multiplexing capability    -   Limited reporter dyes (with non-overlapping emission spectra or        with good spectral resolution)    -   The need for positive controls and spike-ins    -   The need to run a passive reference dye    -   Cost of spike-in controls    -   Cost and added complexity of controls, especially external        controls    -   Sample prep limitations.

Such factors generally limit multiplex qPCR to two to four targets atthe maximum since up to three dyes are used as controls (passivereference, internal, spike-in).

Thus, for cost and practical reasons, there is a need for a bettercontrol strategy in gene expression analysis, and in particular onetailored for use in medical devices.

Prior art practices in the design and use of controls in gene expressionanalyses is limited and is generally based on variations on the themesof the use of spike-ins (artificial sequences and naturally occurringsequences) and internal measured controls. For example, Vandesompele etal., (2002) (Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N,et al. (2002) Accurate normalisation of real-time quantitative RT-PCRdata by geometric averaging of multiple internal control genes. GenomeBiol 3: RESEARCH0034) describe the use of multiple internal controlgenes (a collection of measured biomarkers), rather than just a singleinternal control gene, and a method of identifying stably expressedgenes in different tissues for the use of tissue-specific internalcontrol genes. The authors suggest that different tissues may requirethe use of different internal control genes and that the use of morethan one internal control gene provides more consistent results withrespect to normalisation. Prior to this publication it was generallyaccepted that a single gene was sufficient for normalisation and thatthe genes GAPDH, beta-2 microglobulin or 18S ribosomal were stablyexpressed across all tissues and all conditions, which has since beenproven to be incorrect, especially in conditions that have a largeeffect on gene expression, such as peripheral blood gene expression insepsis.

Fardin et al., (2007) (Fardin P, Moretti S, Biasotti B, Ricciardi A,Bonassi S, et al. (2007) Normalisation of low-density microarray usingexternal spike-in controls: analysis of macrophage cell lines expressionprofile. BMC Genomics 8: 17. doi:10.1186/1471-2164-8-17) describe theuse of artificial spike-in RNAs as a method of providing more consistentnormalisation for low density array qPCR data, especially when thedistribution of up- and down-regulated genes is asymmetric. Similarly,Jiang et al., (2011) (Jiang L, Schlesinger F, Davis C A, Zhang Y, Li R,et al. (2011) Synthetic spike-in standards for RNA-seq experiments.Genome Res 21: 1543-1551. doi:10.1101/gr.121095.111) describe syntheticRNA spike-in controls for use in RNA-seq experiments.

Various published patents describe the use of internal control genes(measured biomarkers) specifically for blood (EP2392668A2,US20100184608) or artificial universal spike-in (external) controls foruse with any tissue type (US20030148339).

In the patent entitled “Diagnostic and Prognostic Tests” (U.S. Pat. No.7,622,260) the inventors describe an approach using ratios of geneexpression to diagnose biological states or conditions, in particularcancer, and for distinguishing malignant pleural mesothelioma from otherlung cancers or from normal lung tissue, and for distinguishing betweensubclasses of malignant pleural mesothelioma.

SUMMARY OF THE PRESENT INVENTION

In one broad form the present invention seeks to provide a method forvalidating quantification of biomarkers, the biomarkers being quantifiedusing a quantification technique of a selected type, and the methodincluding:

-   -   a) determining a plurality of biomarker values, each biomarker        value being indicative of a value measured or derived from a        measured value, for at least one corresponding biomarker of the        biological subject and being at least partially indicative of a        concentration of the biomarker in a sample taken from the        subject;    -   b) determining at least one control value by determining a        combination of biomarker values;    -   c) comparing each control value to a respective control        reference; and,    -   d) determining if the biomarker values are valid using results        of the comparison.

Typically at least first and second biomarker values are used todetermine an indicator indicative of a test result, and wherein themethod includes determining control values including:

-   -   a) a combination of the first and at least one other biomarker        value; and,    -   b) a combination of the second and at least one other biomarker        value.

Typically the method includes:

-   -   a) determining at least four biomarker values, the indicator        being based on a combination of:        -   i) a first indicator value calculated using first and second            biomarker values; and,        -   ii) a second indicator value calculated using third and            fourth biomarker values; and,    -   b) determining control values including:        -   i) a first control value calculated using first and third            biomarker values;        -   ii) a second control value calculated using first and fourth            biomarker values;        -   iii) a third control value calculated using second and third            biomarker values; and,        -   iv) a fourth control value calculated using second and            fourth biomarker values.

Typically the method includes determining control values including:

-   -   a) a fifth control value calculated using first and second        biomarker values;    -   b) a sixth control value calculated using third and fourth        biomarker values; and,    -   c) control values calculated using a combination of measured        biomarkers not used in determining an indicator value.

Typically the method includes calculating at least one of the indicatorvalues and the control values by applying a function to the respectivebiomarker values.

Typically the function includes at least one of:

-   -   a) multiplying two biomarker values;    -   b) dividing two biomarker values;    -   c) a ratio of two biomarker values;    -   d) adding two biomarker values;    -   e) subtracting two biomarker values;    -   f) a weighted sum of at least two biomarker values;    -   g) a log sum of at least two biomarker values; and,    -   h) a sigmoidal function of at least two biomarker values.

Typically the method includes determining:

-   -   a) a first control value using a ratio of first and third        biomarker values;    -   b) a second control value using a ratio of first and fourth        biomarker values;    -   c) a third control value using a ratio of second and third        biomarker values; and,    -   d) a fourth control value using a ratio of second and fourth        biomarker values.

Typically the method includes determining control values including:

-   -   a) a fifth control value using a ratio of first and second        biomarker values;    -   b) a sixth control value using a ratio of third and fourth        biomarker values; and,    -   c) controls values calculated using a ratio of measured        biomarkers not used in determining an indicator value.

Typically the method includes:

-   -   a) determining a validity probability based on the result of the        comparison; and,    -   b) using the validity probability to determine if the biomarker        values are valid.

Typically the method includes:

-   -   a) determining a control value probability for the comparison of        each control value to the respective control reference; and,    -   b) combining the control value probabilities to determine the        validity probability.

Typically the control reference is at least one of:

-   -   a) a control value threshold range;    -   b) a control value threshold; and,    -   c) a control value distribution.

Typically the control reference is a control value threshold range, andwherein the method includes:

-   -   a) comparing each control value to a respective control value        threshold range; and,    -   b) determining at least one of the biomarker values to be        invalid if any one of the control values falls outside the        respective control value threshold range.

Typically the control reference is a control value distribution, andwherein the method includes:

-   -   a) comparing each control value to a respective control value        distribution; and,    -   b) determining the validity using the results of the        comparisons.

Typically each respective reference is derived from biomarker valuescollected from a number of individuals in a sample population.

Typically each respective reference is determined for at least part ofthe sample population.

Typically the sample population includes:

-   -   a) a plurality of individuals of different sexes;    -   b) a plurality of individuals of different ethnicities;    -   c) a plurality of healthy individuals;    -   d) a plurality of individuals suffering from at least one        diagnosed medical condition;    -   e) a plurality of individuals showing clinical signs of at least        one medical condition; and,    -   f) first and second groups of individuals, each group of        individuals suffering from a respective diagnosed medical        condition.

Typically the indicator is for use in determining the likelihood that abiological subject has at least one medical condition, and wherein thesample population includes:

-   -   a) individuals presenting with clinical signs of the at least        one medical condition;    -   b) individuals diagnosed with the at least one medical        condition; and,    -   c) healthy individuals.

Typically the indicator is determined by combining the first and secondderived indicator values using a combining function, the combiningfunction being at least one of:

-   -   a) an additive model;    -   b) a linear model;    -   c) a support vector machine;    -   d) a neural network model;    -   e) a random forest model;    -   f) a regression model;    -   g) a genetic algorithm;    -   h) an annealing algorithm;    -   i) a weighted sum; and,    -   j) A nearest neighbour model.

Typically the method includes:

-   -   a) determining an indicator value;    -   b) comparing the indicator value to at least one indicator value        range; and,    -   c) determining the indicator at least in part using a result of        the comparison.

Typically the indicator is indicative of a likelihood of the subjecthaving at least one medical condition.

Typically the method includes generating a representation of theindicator.

Typically the representation includes:

-   -   a) an alphanumeric indication of the indicator value;    -   b) a graphical indication of a comparison of the indicator value        to one or more thresholds; and,    -   c) an alphanumeric indication of a likelihood of the subject        having at least one medical condition.

Typically the biomarker value is indicative of a level or abundance of amolecule, cell or organism selected from one or more of:

-   -   a) proteins;    -   b) nucleic acids;    -   c) carbohydrates;    -   d) lipids;    -   e) proteoglycans;    -   f) cells;    -   g) metabolites;    -   h) tissue sections;    -   i) whole organisms; and,    -   j) molecular complexes.

Typically the method is performed at least in part using one or moreelectronic processing devices.

Typically the indicator reference is retrieved from a database.

Typically the method includes, in the one or more electronic processingdevices:

-   -   a) receiving the biomarker values;    -   b) determining the at least one control value using at least two        of the biomarker values;    -   c) comparing the at least one control value to the respective        control value threshold; and,    -   d) determining if the test is a valid test using the results of        the comparison.

Typically the method includes, in the one or more electronic processingdevices:

-   -   a) determining the indicator by:        -   i) calculating a first indicator value using a ratio of            first and second biomarker values;        -   ii) calculating a second indicator value using a ratio of            third and fourth second biomarker values; and,        -   iii) determining a sum of the first and second indicator            values;    -   b) determining a plurality of control values by:        -   i) calculating a first control value using a ratio of the            first and third biomarker values;        -   ii) calculating a second control value using a ratio of the            first and fourth biomarker values;        -   iii) calculating a third control value using a ratio of the            second and third biomarker values; and,        -   iv) calculating a fourth control value using a ratio of the            second and fourth biomarker values;    -   c) comparing each control value to a respective threshold range;        and,    -   d) displaying the indicator in response to a successful        comparison for each control value.

Typically the method includes, in the one or more electronic processingdevices:

-   -   a) calculating a fifth control value using a ratio of the first        and second biomarker values;    -   b) calculating a sixth control value using a ratio of the third        and fourth biomarker values; and,    -   c) calculating an additional or set of control values by using a        combination of biomarkers not used in determining an indicator        value.

Typically the biomarkers are gene expression products and wherein themethod includes:

-   -   a) obtaining a sample from a biological subject, the sample        including the gene expression products;    -   b) amplifying at least the gene expression products in the        sample; and,    -   c) for each gene expression product, determining an        amplification amount representing a degree of amplification        required to obtain a defined level of the respective gene        expression.

Typically the amplification amount is at least one of:

-   -   a) a cycle time;    -   b) a number of cycles;    -   c) a cycle threshold; and,    -   d) an amplification time.

Typically the biomarkers are gene expression products and wherein themethod includes, determining a combination of biomarker values bysubtracting amplification amounts for the respective gene expressionproducts so that the combination of biomarker values represents a ratioof the relative concentration of the respective gene expressionproducts.

Typically the biomarker values are obtained from a biological subjectpresenting with clinical signs of at least one medical condition.

Typically the at least one condition includes ipSIRS (infection positiveSystemic Inflammatory Response Syndrome) and wherein the biomarkervalues correspond to relative concentrations of LAMP1, CEACAM4, PLAC8and PLA2G7.

Typically the biomarker values are obtained from a biological subjectpresenting with clinical signs common to first and second conditions andwherein the indicator is for use in distinguishing between the first andsecond conditions.

Typically the first and second conditions include inSIRS (infectionnegative Systemic Inflammatory Response Syndrome) and ipSIRS.

Typically the quantification technique is at least one of:

-   -   a) a nucleic acid amplification technique;    -   b) polymerase chain reaction (PCR);    -   c) a hybridisation technique;    -   d) microarray analysis;    -   e) low density arrays;    -   f) hybridisation with allele-specific probes;    -   g) enzymatic mutation detection;    -   h) ligation chain reaction (LCR);    -   i) oligonucleotide ligation assay (OLA);    -   j) flow-cytometric heteroduplex analysis;    -   k) chemical cleavage of mismatches;    -   l) mass spectrometry;    -   m) flow cytometry;    -   n) liquid chromatography;    -   o) gas chromatography;    -   p) immunohistochemistry;    -   q) nucleic acid sequencing;    -   r) single strand conformation polymorphism (SSCP);    -   s) denaturing gradient gel electrophoresis (DGGE);    -   t) temperature gradient gel electrophoresis (TGGE);    -   u) restriction fragment polymorphisms;    -   v) serial analysis of gene expression (SAGE);    -   w) affinity assays;    -   x) radioimmunoassay (RIA);    -   y) lateral flow immunochromatography;    -   z) flow cytometry;    -   aa) electron microscopy (EM); and,    -   bb) enzyme-substrate assay.

In one broad form the present invention seeks to provide apparatus forvalidating measurement of biomarker values used in generating anindicator, the biomarkers being quantified using a quantificationtechnique of a selected type, and the apparatus including at least oneprocessing device that:

-   -   a) determines a plurality of biomarker values, each biomarker        value being indicative of a value measured or derived from a        measured value, for at least one corresponding biomarker of the        biological subject and being at least partially indicative of a        concentration of the biomarker in a sample taken from the        subject;    -   b) determines at least one control value by determining a        combination of biomarker values;    -   c) compares each control value to a respective control        reference; and,    -   d) determines if the biomarker values are valid using results of        the comparison.

In one broad form the present invention seeks to provide a method forvalidating an indicator used in determining the likelihood of abiological subject having at least one medical condition, the biomarkersbeing quantified using a quantification technique of a selected type andthe method including:

-   -   a) determining a plurality of biomarker values, each biomarker        value being indicative of a value measured or derived for at        least one corresponding biomarker of the biological subject;    -   b) determining an indicator indicative of the likelihood of a        biological subject having at least one medical condition by:        -   i) calculating a first indicator value using first and            second biomarker values;        -   ii) calculating a second indicator value using third and            fourth second biomarker values; and,        -   iii) determining the indicator using the first and second            indicator values;    -   c) calculating at least one of:        -   i) a first control value using the first and third biomarker            values;        -   ii) a second control value using the first and fourth            biomarker values;        -   iii) a third control value using the second and third            biomarker values;        -   iv) a fourth control value using the second and fourth            biomarker values;        -   v) a fifth control value using the first and fourth second            values;        -   vi) a sixth control value using the third and fourth            biomarker values; and,        -   vii) an additional or set of control values by using a            combination of biomarkers not used in determining an            indicator value;    -   d) comparing the at least one control value to a respective        control value threshold; and,    -   e) selectively validating the indicator using the results of the        comparison.

In one broad form the present invention seeks to provide apparatus forvalidating an indicator indicative of measured values of gene expressionproducts, the biomarkers being quantified using a quantificationtechnique of a selected type, the apparatus including at least oneprocessing device that:

-   -   a) determines a plurality of biomarker values, each biomarker        value being indicative of a value measured or derived for at        least one corresponding biomarker of the biological subject;    -   b) determines the indicator by:        -   i) calculating a first indicator value using first and            second biomarker values;        -   ii) calculating a second indicator value using third and            fourth second biomarker values; and,        -   iii) determining the indicator using the first and second            indicator values;    -   c) calculates at least one of:        -   i) a first control value using the first and third biomarker            values;        -   ii) a second control value using the first and fourth            biomarker values;        -   iii) a third control value using the second and third            biomarker values;        -   iv) a fourth control value using the second and fourth            biomarker values;        -   v) a fifth control value using the first and second            biomarker values;        -   vi) a sixth control value using the third and fourth            biomarker values; and,        -   vii) an additional or set of control values by using a            combination of biomarkers not used in determining an            indicator value.    -   d) compares the at least one control value to a respective        control value threshold; and,    -   e) selectively validates the indicator using the results of the        comparison.

In one broad form the present invention seeks to provide a method forvalidating an indicator used in determining the likelihood of abiological subject having at least one medical condition, the biomarkersbeing quantified using a quantification technique of a selected type andthe method including:

-   -   a) obtaining a sample from a biological subject, the sample        including gene expression products;    -   b) quantifying at least some gene expression products in the        sample to determine a concentration of the gene expression        product in the sample;    -   c) determining an indicator indicative of the likelihood of a        biological subject having at least one medical condition by        combining:        -   i) a first indicator value indicative of a ratio of the            concentration of the first and second gene expression            products; and,        -   ii) a second indicator value indicative of a ratio of the            concentration of the third and fourth gene expression            products;    -   d) determining control values by determining at least one of:        -   i) a first control value indicative of a ratio of the            concentration of the first and third gene expression            products;        -   ii) a second control value indicative of a ratio of the            concentration of the first and fourth gene expression            products;        -   iii) a third control value indicative of a ratio of the            concentration of the second and third gene expression            products;        -   iv) a fourth control value indicative of a ratio of the            concentration of the second and fourth gene expression            products;        -   v) a fifth control value indicative of a ratio of the            concentration of the first and second gene expression            products;        -   vi) a sixth control value indicative of a ratio of the            concentration of the third and fourth gene expression            products; and,        -   vii) an additional or set of control values by using a            combination of gene expression products not used in            determining an indicator value;    -   e) comparing each control value to a respective control value        threshold range; and,    -   f) validating the indicator if each of the control values is        within the respective control value range.

Typically the method includes quantifying the concentration of the geneexpression products by:

-   -   a) amplifying at least some gene expression products in the        sample; and,    -   b) for each of a plurality of gene expression products,        determining an amplification amount representing a degree of        amplification required to obtain a defined level of the        respective gene expression product.

Typically the method includes:

-   -   a) determining the indicator by:        -   i) determining a first indicator value calculated using the            first and second amplification times indicative of the            concentration of first and second gene expression products;            and,        -   ii) a second indicator value calculated using third and            fourth amplification times indicative of the relative            concentration of third and fourth gene expression products;            and,    -   b) determining control values by determining at least one of:        -   i) a first control value calculated using first and third            amplification times indicative of the relative concentration            of first and third gene expression products;        -   ii) a second control value calculated using first and fourth            amplification times indicative of the relative concentration            of first and fourth gene expression products;        -   iii) a third control value calculated using second and third            amplification times indicative of the relative concentration            of second and third gene expression products;        -   iv) a fourth control value calculated using second and            fourth amplification times indicative of the relative            concentration of second and fourth gene expression products;        -   v) a fifth control value calculated using first and second            amplification times indicative of the relative concentration            of first and second gene expression products;        -   vi) a sixth control value calculated using third and fourth            amplification times indicative of the relative concentration            of third and fourth gene expression products; and        -   vii) an additional or set of control values by using a            combination of amplification times not used in determining            an indicator value.

In one broad form the present invention seeks to provide apparatus forvalidating an indicator used in determining the likelihood of abiological subject having at least one medical condition, the apparatusincluding:

-   -   a) a sampling device that obtains a sample from a biological        subject, the sample including gene expression products;    -   b) a quantification device that quantifies at least some gene        expression products in the sample to determine a concentration        of the gene expression product in the sample; and,    -   c) at least one processing device that:        -   i) determines an indicator indicative of the likelihood of a            biological subject having at least one medical condition by            combining:            -   (1) a first indicator value indicative of a ratio of the                concentration of the first and second gene expression                products; and,            -   (2) a second indicator value indicative of a ratio of                the concentration of the third and fourth gene                expression products;        -   ii) determines control values by determining at least one            of:            -   (1) a first control value indicative of a ratio of the                concentration of the first and third gene expression                products;            -   (2) a second control value indicative of a ratio of the                concentration of the first and fourth gene expression                products;            -   (3) a third control value indicative of a ratio of the                concentration of the second and third gene expression                products;            -   (4) a fourth control value indicative of a ratio of the                concentration of the second and fourth gene expression                products;            -   (5) a fifth control value indicative of a ratio of the                concentration of the first and second gene expression                products;            -   (6) a sixth control value indicative of a ratio of the                concentration of the third and fourth gene expression                products; and,            -   (7) an additional or set of control values indicative of                a ratio of the concentration of biomarkers not used in                determining an indicator value;        -   iii) compares each control value to a respective control            value threshold range; and,        -   iv) validates the indicator if each of the control values is            within the respective control value range.

In one broad form the present invention seeks to provide a method forvalidating quantification of biomarkers, and the method including:

-   -   a) determining a plurality of biomarker values, each biomarker        value being indicative of a value measured or derived from a        measured value, for at least one corresponding biomarker of the        biological subject and being at least partially indicative of a        concentration of the biomarker in a sample taken from the        subject;    -   b) determining at least one control value by determining a        combination of biomarker values;    -   c) comparing each control value to a respective control        reference; and,    -   d) determining if the biomarker values are valid using results        of the comparison, wherein the biomarker value is indicative of        a level or abundance of a molecule, cell or organism selected        from one or more of:        -   i) proteins;        -   ii) nucleic acids;        -   iii) carbohydrates;        -   iv) lipids;        -   v) proteoglycans;        -   vi) cells; and,        -   vii) pathogenic organisms.

In one broad form the present invention seeks to provide a method forvalidating quantification of biomarkers, the method including:

-   -   a) determining a plurality of biomarker values, each biomarker        value being indicative of a value measured or derived from a        measured value, for at least one corresponding biomarker of the        biological subject and being at least partially indicative of a        concentration of the biomarker in a sample taken from the        subject;    -   b) determining at least one control value by determining a        combination of biomarker values;    -   c) comparing each control value to a respective control        reference; and,    -   d) determining if the biomarker values are valid using results        of the comparison, wherein the biomarker value is indicative of        a level or abundance of a molecule, cell or organism selected        from one or more of:        -   i) proteins;        -   ii) nucleic acids;        -   iii) carbohydrates;        -   iv) lipids;        -   v) proteoglycans;        -   vi) cells;        -   vii) metabolites;        -   viii) tissue sections;        -   ix) whole organisms; and,        -   x) molecular complexes.

In one broad form the present invention seeks to provide a method forvalidating quantification of biomarkers, the method including:

-   -   a) determining a plurality of biomarker values, each biomarker        value being indicative of a value measured or derived from a        measured value, for at least one corresponding biomarker of the        biological subject and being at least partially indicative of a        concentration of the biomarker in a sample taken from the        subject;    -   b) determining at least one control value by determining a        combination of biomarker values;    -   c) comparing each control value to a respective control        reference; and,    -   d) determining if the biomarker values are valid using results        of the comparison, wherein the biomarkers are quantified using        at least one of:        -   i) a nucleic acid amplification technique;        -   ii) polymerase chain reaction (PCR);        -   iii) a hybridisation technique;        -   iv) microarray analysis;        -   v) low density arrays;        -   vi) hybridisation with allele-specific probes;        -   vii) enzymatic mutation detection;        -   viii) ligation chain reaction (LCR);        -   ix) oligonucleotide ligation assay (OLA);        -   x) flow-cytometric heteroduplex analysis;        -   xi) chemical cleavage of mismatches;        -   xii) mass spectrometry;        -   xiii) flow cytometry;        -   xiv) liquid chromatography;        -   xv) gas chromatography;        -   xvi) immunohistochemistry;        -   xvii) nucleic acid sequencing;        -   xviii) single strand conformation polymorphism (SSCP);        -   xix) denaturing gradient gel electrophoresis (DGGE);        -   xx) temperature gradient gel electrophoresis (TGGE);        -   xxi) restriction fragment polymorphisms;        -   xxii) serial analysis of gene expression (SAGE);        -   xxiii) affinity assays;        -   xxiv) radioimmunoassay (RIA);        -   xxv) lateral flow immunochromatography;        -   xxvi) flow cytometry;        -   xxvii) electron microscopy (EM); and,        -   xxviii) enzyme-substrate assay.

It will be appreciated that the broad forms of the invention and theirrespective features can be used in conjunction, interchangeably and/orindependently, and reference to separate broad forms is not intended tobe limiting.

BRIEF DESCRIPTION OF THE DRAWINGS

An example of the present invention will now be described with referenceto the accompanying drawings, in which:—

FIG. 1A is a flow chart of an example of a method for validatingmeasurement of biomarker values;

FIGS. 1B and 1C are flow charts of an example of the comparison ofindependent and relative control approaches;

FIG. 2 is a schematic diagram of an example of a distributed computerarchitecture;

FIG. 3 is a schematic diagram of an example of a base station processingsystem;

FIG. 4 is a schematic diagram of an example of a client device of FIG.2;

FIG. 5 is a flowchart of an example of a method for validating anindicator derived from biomarker measurements and correspondingreference distributions;

FIG. 6 is a flowchart of an example of a method for validating anindicator derived from biomarker measurements;

FIG. 7A is schematic diagram of an indication of the relationship ofbiomarker values in the process of FIG. 5;

FIG. 7B is schematic diagram of an indication of the relationship ofbiomarker values to a control in a standard control arrangement;

FIGS. 8A and 8B are a flowchart of an example of a method for validatingan indicator derived from biomarker measurements;

FIGS. 9A and 9B are schematic diagrams of examples of representations ofindicator values;

FIG. 10A is a flow chart of an example of the standard use of controlsin a multi-biomarker medical device;

FIG. 10B is a flow chart of an example of the use of relative controlsin place of standard controls in a multi-biomarker medical device;

FIG. 10C is a flow chart of an example of the us of a hybrid of standardand relative controls in a multi-biomarker medical device

FIG. 11A is plots of cycle times obtained for measured biomarkers over arange of concentrations;

FIG. 11B is plots indicator values for biomarker values derived from thecycle times of FIG. 11A;

FIG. 12A is a density plot of measured biomarker values for a samplepopulation;

FIG. 12B (a)-(f) are plots of each measured biomarker value for aninvalid sample shown against the reference population of measuredbiomarkers;

FIG. 12C (a)-(f) are plots of derived control values for an invalidsample shown against the reference derived control values.

FIG. 12D is a scatterplot showing the invalid sample against one of thereference derived control biomarkers.

FIG. 13A (a)-(d) are plots showing an invalid sample against a referencepopulation of measured biomarkers.

FIG. 13B (a)-(f) are plots showing the same invalid sample against areference population of derived control biomarkers.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

An example of a process for validating measurement of biomarkers for usein determining an indicator, such an as an indicator indicative of thelikelihood of a biological subject having at least one predominantmedical condition will now be described with reference to FIG. 1.

For the purpose of explanation, a number of different terms will beused.

For example, the term “biomarker” refers to any quantifiable value, orcombination or derivative of parameters, that can be used as anindicator of a biological state. In the context of the currentapplication, biomarkers include proteins, nucleic acids, such as DNA,RNA or the like, carbohydrates, lipids, proteoglycans, cells,metabolites, tissue sections, whole organisms (e.g. pathogenic andnon-pathogenic microorganisms) and molecular complexes (e.g.protein/nucleic acid complex), or the like.

The term “biomarker value” refers to a value determined by quantifyingthe amount of, abundance of, level of, concentration of, quantity of, oractivity of, the corresponding biomarker within a subject or individual.The biomarker value can be based on a measured biomarker value or avalue derived therefrom, and examples will be described in more detailbelow.

The term “reference biomarkers” is used to refer to biomarkers whosevalues are known for a sample population of one or more individualshaving one or more conditions, stages of one or more conditions,subtypes of one or more conditions or different prognoses. The term“reference data” refers to data measured for one or more individuals ina sample population, and may include quantification of the level oractivity of the biomarkers measured for each individual, informationregarding any conditions of the individuals, and optionally any otherinformation of interest including derived biomarkers which have beenderived from measured markers. Reference biomarkers are named for theirprimary purpose of providing a reference against which new or unknownsamples can be compared.

The term “indicator values” is used to refer to combinations ofbiomarker values that are used in deriving an indicator, which may beindicative of the likelihood of a subject suffering from a biologicalcondition. The indicator could be in the form of an absolute or relativenumerical or other value, and could be based on comparison of a value toone or more thresholds.

The term “test” is used to refer to mechanism that is used inquantifying a plurality of biomarkers to determine respective biomarkervalues, which can then be used subsequently in determining indicatorvalues. The “test” could include one or more measurement processes orsteps, that could be performed collectively or independently, but whichare performed using a quantification platform or technique of a selectedtype. The “test” may form a part of a broader “medical assessment”,which could include a number of different tests, performed to allow forthe diagnosis of a presence, absence, degree or prognosis associatedwith a medical condition.

The terms “quantification platform of a selected type” and“quantification technique of a selected type” are used interchangeablyherein to refer to a device and/or method or combination of devicesand/or methods that can determine the amount of, abundance of, level of,concentration of, quantity of, or activity of, one or more biomarkers ofinterest where either quality control measures are used as part of theoverall procedure, or the use of control(s) is/are used. Representativeexamples of such include nucleic acid amplification techniques includingpolymerase chain reaction (PCR) (e.g., PCR-based methods such as realtime polymerase chain reaction (RT-PCR), quantitative real timepolymerase chain reaction (Q-PCR/qPCR), use of PCR to analyse chromatinconformation (CCA), and the like), hybridisation techniques includingmicroarray analysis, low density arrays, hybridisation withallele-specific probes, enzymatic mutation detection, ligation chainreaction (LCR), oligonucleotide ligation assay (OLA), flow-cytometricheteroduplex analysis, chemical cleavage of mismatches, massspectrometry, flow cytometry, liquid chromatography, gas chromatography,immunohistochemistry, nucleic acid sequencing (including next generationsequencing, ChIP-seq, DNA methylation analyses), single strandconformation polymorphism (SSCP), denaturing gradient gelelectrophoresis (DGGE), temperature gradient gel electrophoresis (TGGE),restriction fragment polymorphisms, serial analysis of gene expression(SAGE), affinity assays including immunoassays such as immunoblot,immunoprecipitation, enzyme-linked immunosorbent assay (ELISA; EIA),lateral flow immunochromatography, radioimmunoassay (RIA), electronmicroscopy (EM), enzyme-substrate assay, or combinations thereof.

The term “control” is used to refer to a mechanism utilised on order todetermine a pass or fail state for the validity of a test, and thereforethe validity of the output.

Controls can include “independent controls”, which are added to a testand are independent of the biomarkers being quantified. Thus, theindependent controls are independent of the measured biomarkers and canbe considered a stand-alone test for the validity of the test overall.An example is a synthetically produced in vitro transcript in a geneexpression test at a known concentration. In this case, the sample beingtested (ie blood) does not interact at all with the independent control.The control serves only to ensure that the reagents used across thewhole test are capable of reproducing a value for this independentcontrol to the expected value.

The term “control values” is used to refer to combinations of biomarkervalues or indicators that are used in assessing whether biomarkervalues, such as the biomarker values used to derive indicator values andthe resulting indicator values, are valid. In this regard, a biomarkervalue or indicator value may be invalid if it has been incorrectlymeasured, calculated or quantified and as such is not genuinelyindicative of the target condition, or if it's value is sufficientlyrarely represented in the corresponding reference data that it could bereasonably presumed as true that the value could not have derived from asuccessful test and therefore the assay should be declared invalid (inthe case of a failed control). Examples of p values that may beconsidered presumed to be true range from:

-   -   0.50 to 0.20    -   0.20 to 0.10    -   0.10 to 0.05    -   0.05 to 0.01    -   0.01 to 0.001    -   0.001 to zero.

Control values are “Relative Controls” that define a pass or fail stateof a test that are not independent of the biomarkers being quantified.For example, if there are two markers measured in the test marker A andmarker B, then one way in which these markers may be relative to eachother is the ratio of marker A to marker B. This relationship is acontrol if its value is used to pass or fail the validity of a test. Inthis example, if the ratio of marker A to marker B is a value outside ofan acceptable range, the test will be declared invalid.

A “positive control” is used to show that the test is able to produce apositive result. Typically the positive control is designed so that whenexposed to the same treatment as the other markers being measured itwill result in a detection at a certain level. The assumption is that ifthe treatment worked acceptably for the positive control, then it alsoworked for the other assays in the test. An example of where this willbe useful is in the case where the test has been exposed to unacceptabletemperatures during transport, which has destroyed some key ingredientin the test. With a key ingredient destroyed, the positive control willnot work as expected and the test will be declared invalid.

A “negative control” is used to show that the test is able to produce anegative result. Typically the negative control is designed so that whenexposed to the same treatment as the other markers being measured itwill result in a detection below a certain level (usually below thedetectable limit of the test). The assumption is that if the treatmentdid not result in positive detection for the negative control, then theother assays in the test are also capable of a negative detection.

The terms “biological subject”, “subject,” “individual” and “patient”are used interchangeably herein to refer to an animal subject,particularly a vertebrate subject, and even more particularly amammalian subject. Suitable vertebrate animals that fall within thescope of the invention include, but are not restricted to, any member ofthe subphylum Chordata including primates, rodents (e.g., mice rats,guinea pigs), lagomorphs (e.g., rabbits, hares), bovines (e.g., cattle),ovines (e.g., sheep), caprines (e.g., goats), porcines (e.g., pigs),equines (e.g., horses), canines (e.g., dogs), felines (e.g., cats),avians (e.g., chickens, turkeys, ducks, geese, companion birds such ascanaries, budgerigars etc.), marine mammals (e.g., dolphins, whales),reptiles (snakes, frogs, lizards, etc.), and fish. A preferred subjectis a primate (e.g., a human, ape, monkey, chimpanzee).

As used herein, the term SIRS (“systemic inflammatory responsesyndrome”) refers to a clinical response arising from a non-specificinsult with two or more of the following measurable clinicalcharacteristics; a body temperature greater than 38° C. or less than 36°C., a heart rate greater than 90 beats per minute, a respiratory rategreater than 20 per minute, a white blood cell count (total leukocytes)greater than 12,000 per mm³ or less than 4,000 per mm³, or a bandneutrophil percentage greater than 10%. From an immunologicalperspective, it may be seen as representing a systemic response toinsult (e.g., major surgery) or systemic inflammation. As used herein,“inSIRS” (which includes within its scope “post-surgical” (PS)inflammation) includes the clinical response noted above but in theabsence of a systemic infectious process (infection-negative systemicinflammatory response syndrome). By contrast, “ipSIRS”(infection-positive systemic inflammatory response syndrome) includesthe clinical response noted above but in the presence of a presumed orconfirmed infection. Presumed infection can be based on clinician'sjudgement whereas confirmation of an infection can be determined usingmicrobiological culture, isolation or detection of the infectious agentor through the use of other parameters that provide evidence ofinfection. From an immunological perspective, ipSIRS may be seen as asystemic response to microorganisms, be it a local, peripheral orsystemic infection.

As used herein, the term “likelihood” of a condition refers to a levelof certainty associated with whether or not the subject may be sufferingfrom a condition. It should be noted that this does not necessarilycorrelate with a degree, seriousness, severity, stage or state of acondition.

It will be appreciated that the above described terms and associateddefinitions are used for the purpose of explanation only and are notintended to be limiting.

In this example, the method includes determining a plurality ofbiomarker values at step 100, each biomarker value being indicative of avalue measured or derived for at least one biomarker of the biologicalsubject.

The biomarker values can be of any appropriate form and in particularcan relate to any attribute of a subject for which a value can bequantified. This technique is particularly suited to high-throughputtechnologies such as mass spectrometry, sequencing platforms, array andhybridisation platforms, immunoassays, flow cytometry, and in onepreferred example, the biomarker values relate to a level of activity orabundance of an expression product or other measurable molecule.

The biomarker values could be measured biomarker values, which arevalues of biomarkers measured for the subject, or alternatively could bederived biomarker values, which are values that have been derived fromone or more measured biomarker values, for example by applying afunction to the one or more measured biomarker values. As used herein,biomarkers to which a function has been applied are referred to as“derived biomarkers”.

The biomarker values may be determined in any one of a number of ways.In one example, the process of determining the biomarker values caninclude measuring the biomarker values, for example by obtaining asample from the biological subject and then quantifying the biomarkerswithin the sample. More typically however, the step of determining thebiomarker values includes having an electronic processing device receiveor otherwise obtain biomarker values that have been previously measuredor derived. This could include for example, retrieving the biomarkervalues from a data store such as a local or remote instrument ordatabase, obtaining biomarker values that have been manually input,using an input device, or the like.

At step 110 an indicator can optionally be determined with the indicatorbeing at least partially based on the biomarker values. The indicator isgenerally indicative of a test result and can be determined in any oneof a number of ways and may be at least partially based on a ratio ofbiomarker values, as will be described in more detail below. However,this is not essential and alternatively the biomarker values could beused to validate that the quantification has been performed correctly,with indicators or other interpretation of the biomarker values beingperformed in subsequent downstream processes.

At step 120 one or more control values are determined. The controlvalues are determined based on a combination of the biomarker values.The biomarker values can be combined in any one of a number of ways andthis can include for example adding, multiplying, subtracting, ordividing biomarker values to determine the control value. This step isperformed so that multiple biomarker values can be combined into asingle control value, and typically a self-normalised value, as will bedescribed in more detail below.

At step 130 each control value is compared to a respective controlreference. The respective control reference is typically establishedbased on reference control values determined for a sample populationincluding a mixture of healthy individuals and individuals sufferingfrom or demonstrating clinical signs of one or more conditions. Thecontrol reference can be a single threshold value or a range defined byrespective upper and lower values but more typically is in the form ofdistribution of control values.

At step 140 measurements of the biomarker values are validated usingresults of the comparison. Thus, if any control values are beyond/underthe threshold, outside of a defined threshold range, or beyond a certainpoint in the threshold, or beyond a certain point of the distribution,this is used to indicate that the ascertained biomarker values measuredare not suitable for use in generating an indicator that is reliableenough for use in determining the likelihood of a condition.

Accordingly, the above technique uses different combinations ofbiomarker values to identify if biomarker values are valid.

In one example, the control values are based on a combination ofbiomarker values, which differs to a combination of biomarkers used toestablish an indicator indicative of a test result. For example, ifvalues are quantified for three biomarkers for the subject, namely A, Band C, and the biomarker values A and B are used to establish theindicator, then combinations of A and C and B and C can be used todetermine the control values.

In this example, if measurement of biomarker A is spurious, for example,due to failures in acquiring, storing or processing of a sample from thesubject, or the like, this could result in an indicator value based onthe combination of biomarkers A and B which is indicative of the subjecthaving or not having a condition. However, in reality, because themeasurement biomarker A is incorrect, this result is meaningless, andhence could lead to inaccurate diagnosis if relied upon.

In this case, by also determining values of control values using thecombinations of A and C and B and C, it will be identified that thecontrol value corresponding to A and C is outside an expected range forindividuals either having or not having a condition of interest, meaningthat the biomarker values for A and/or B are not valid, and hence can'tbe used in establishing an accurate indicator.

Thus, the above described process recognises that biomarkers values aretypically within defined ranges for individuals regardless or not ofwhether they are suffering from conditions. Thus by measuring variouscombinations of different biomarker values, and comparing these toestablished ranges for a reference population of individuals having arange of different conditions, including healthy individuals, this canbe used to establish whether the biomarker values are within expectedranges.

It will be further appreciated that whilst this could in theory beperformed using individual biomarkers as opposed to combinations, thiswould require the ability to measure absolute values, such as absoluteconcentrations of biomarkers within a sample, which generally cannot beachieved. This is typically addressed through the use of independentcontrols, so that the concentration of biomarkers relative to a controlof known concentration is measured. However, the use of such independentcontrols is typically expensive, as the control biomarkers themselvesare difficult to produce, introduce complexity, and also limit thenumber of biomarkers that can be measured by the ability of themeasuring procedures, so as more controls are introduced, this reducesthe number of biomarkers that can be measured for the subject. However,by using combinations of biomarker values, such as ratios, or the like,this allows the measured biomarker values to be indicative of relativeconcentrations, and hence self-normalising. In particular, if theultimate output is based on, for example, ratios of genes, thenmeasurements of validity using similar ratios of genes is moreintuitive, robust, and appropriate. Thus, by comparing differentcombinations of biomarker values to thresholds, this allows checks to beperformed of the validity of the measured value in the nativemeasurement space (i.e. ratios), essentially leading to aself-validating test without the need for measurement of independentcontrols.

Such an approach provides a better control strategy. Using thebiomarkers being measured as controls specifically addresses issuesassociated with normalising results, improves the statistical power forthe detection of failed assays, reduces the overall number of controlsused, reduces the complexity of an assay and reduces overall assay costand risk.

Firstly and by example, by using the described control strategy, manybiomarkers can be used to define derived biomarkers for use as controlranges against a corresponding reference range for each derivedbiomarker. These biomarkers need not be those involved in the indictorbiomarkers used for classification of the patient for the condition ofinterest. Using many relative internal biomarkers for this purpose has asmoothing and stabilizing effect on normalisation thereby reducingoverall variance.

Secondly, by relying on external or spike-in controls, if there is afailure of these controls, the assay will be called invalid, even if theresult from the measured genes, and therefore of the indicator value, isaccurate. Thirdly, by measuring multiple interactions between measuredbiomarkers by looking at the larger number of relative biomarkersavailable, there are more relevant control checks for each biomarkerbeing measured, resulting in higher statistical power, confidence andsensitivity. Fourthly, by avoiding the use of external controls, or useof extraneous housekeeping controls, the complexity of the assay isreduced which translates to decreased cost and risk.

In particular, this technique can avoid the need for independentcontrols, by using control values derived from measured biomarkers ofinterest to self validate a test. This approach is exemplified bycomparison of the independent and relative control approaches, shown inFIGS. 1B and 1C.

As shown in this example, in each case, biomarker values are measured atsteps 151, 161 and used to generate indicator values at steps 152, 162.In the dependent controls process, separate controls are measured atstep 153 and assessed to determine if these are in an expected range atstep 154. In contrast, in the relative controls approach, the measuredbiomarker values are used to derive control values at step 163, whichare then assessed to determine if they are within the expected range atstep 164. In each case, if the control is in range, the test results arereported at steps 155, 165, otherwise the test is failed at step 156,166.

Thus, it can be seen the relative controls formed from control valuesderived from the measured biomarker values can be used in a mannersimilar to independent controls, but without requiring the presence ofindependent controls. This avoids the need for additional controlmarkers, meaning the test can be cheaper. This also avoids the need foradded independent controls failing independently, which can needlesslyinvalidate a valid test. Additionally, relationships between measuredmarkers put tighter and more numerous constraints on expected values,thus increasing statistical power and therefore confidence in detectionof an invalid test, as will be described in more detail below.

A number of further features will now be described.

In one example, at least three biomarker values are used, with first andsecond biomarker values being used to determine the indicator and withthe control values being determined using a combination of the first andat least one other biomarker value and the second and at least one otherbiomarker value. However, in another preferred example, the methodincludes determining at least four biomarker values. In this case, theindicator can be based on a combination of a first indicator valuecalculated using first and second biomarker values and a secondindicator value calculated using third and fourth biomarker values.These two indicator values can then be combined to form the indicator,which combines the discriminatory power of each of the first twoindicator values. This allows two independent pairs of biomarker valuesto be combined and used to establish the indicator, which cansignificantly enhance the ability of the indicator to discriminate thelikelihood of the subject having the condition.

Furthermore, when using four biomarker values, this allows at least fourcontrol values to be determined including a first control valuecalculated using first and third biomarker values, a second controlvalue calculated using first and fourth biomarker values, a thirdcontrol value calculated using second and third biomarker values and afourth control value calculated using second and fourth biomarkervalues. Thus, again, this allows for additional control values to beutilised, further increasing the likelihood that invalid measurementscan be accurately discriminated. It will be appreciated thatcombinations of biomarkers comprising the indicator value can also becontrol values: in this example the first and second biomarkers and thethird and fourth biomarkers make up the indicator value, and they too,if out of range to a corresponding reference, may indicate failure ofthe assay.

It will also be appreciated that in the above example, each of thebiomarker values used in establishing the indicator are also used in thevalidation check. This maximises the use of biomarkers, so that ineffect each measured biomarker value is used in both generating theindicator and the validation. For platforms and processes that can onlyhandle limited numbers of biomarker values, this can therefore maximisethe discriminatory power of the indicator, by allowing all measuredbiomarker values to be used in determining the indicator, whilst stillensuring indicator validity. However, this is not essential, andadditionally and/or alternatively, comparison to a biomarker valuemeasured for the subject, but not used in generating the indicator couldbe performed.

It should also be noted that the indicator values could also be used ascontrol values. In this instance, typically an acceptable range forindicator values would be specified for assessing the likelihood of asubject having a condition, with this range representing the maximum andminimum indicator values observed or expected in the target population.Values outside of this range may imply a problem with at least one ofthe underlying values comprising the indicator value, and the test willbe declared invalid. Accordingly, in this example, the method includesdetermining control values including one or more of a fifth controlvalue using a ratio of first and second biomarker values, a sixthcontrol value using a ratio of third and fourth biomarker values and, asingle or set of controls values calculated using a ratio of measuredbiomarkers not used in determining an indicator value.

The method typically includes calculating at least one of the indicatorvalues and the control values by applying a function to the respectivebiomarker values. The function used will therefore vary depending on thepreferred implementation. In one example, the function includes at leastone of multiplying two biomarker values, dividing two biomarker values;adding two biomarker values, subtracting two biomarker values, aweighted sum of at least two biomarker values, a log sum of at least twobiomarker values and, a sigmoidal function of at least two biomarkervalues.

More typically the function is division of two biomarker values, or logsubtraction (which is equivalent to division of absolute values) so thatthe derived biomarker value corresponds to a ratio of two measuredbiomarker values. There are a number of reasons why the ratio might bepreferred. For example, use of a ratio is self-normalising, meaningvariations in measuring techniques will automatically be accommodated.For example, if the input concentration of a sample is doubled, therelative proportions of biomarkers will remain the same. As a result,the type of function therefore has a stable profile over a range ofinput concentrations, which is important because input concentration isa known variable for expression data. Additionally, many biomarkers arenodes on biochemical pathways, so the ratio of biomarkers givesinformation about the relative activation of one biological pathway toanother, which is a natural representation of biological change within asystem. Finally, ratios are typically easily interpreted.

In one example, the control values are ratios, with each control valuebeing compared to a respective control value threshold range anddetermining at least one of the biomarker values to be invalid if anyone of the control values falls outside the respective control valuethreshold range. In this instance, each respective threshold range istypically derived from biomarker values collected from a number ofindividuals in a sample population. This can be performed for exampleusing a statistical method or computer-implemented classifier algorithmtrained on biomarker values for the sample population. The samplepopulation typically includes a plurality of healthy individuals, aplurality of individuals suffering from at least one diagnosed medicalcondition, a plurality of individuals showing clinical signs of at leastone medical condition or first and second groups of individuals, eachgroup of individuals suffering from a respective diagnosed medicalcondition. This can be used to provide a suitable cross section of thepopulation and to ensure that the control value threshold ranges are notinfluenced by the presence or absence of conditions.

In particular, when an indicator is for use in determining thelikelihood that a biological subject has a specific medical condition,the sample population includes individuals presenting with clinicalsigns of the specific medical condition, individuals diagnosed orconfirmed to have or have had (including retrospectively) the specificmedical condition and/or healthy individuals. This ensures that theassessment of indicator validity applies regardless of not or whetherthe individual has the specific condition or not.

It will also be appreciated that the sample population could alsoinclude a plurality of individuals of different sexes, ethnicities,ages, or the like, allowing the control value ranges to be common acrosspopulations. However, this is not essential, and alternatively controlvalue thresholds could be established that are specific to a particularsub-set of the population. In this case, it would be necessary to ensurethat the control value threshold ranges used are appropriate for thesubject under consideration.

Typically the indicator is determined by combining the first and secondderived indicator values using a combining function, the combiningfunction being at least one of an additive model, a linear model, asupport vector machine, a neural network model, a random forest model, aregression model, a genetic algorithm, an annealing algorithm, aweighted sum and a nearest neighbour model.

In one example, the method further includes determining an indicatorvalue, comparing the indicator value to at least one indicator valuerange and determining the indicator at least in part using a result ofthe comparison. Thus, once it has been established that the biomarkervalues are suitable for use in determining the indicator, the indicatorcan be calculated and compared to an indicator value range to assess thelikelihood of the subject having at least one medical condition.

Following this, the method can further include generating arepresentation of the indicator. In this regard, the representationallows the indicator to be viewed, for example by a medicalpractitioner, allowing the medical practitioner to perform a diagnosisand assess what intervention, if any, to perform. The representation canbe of any appropriate form and can include one or more of analphanumeric indication of an indicator value, a graphical indication ofa comparison of the indicator value to one or more thresholds and analphanumeric indication of a likelihood of the subject having at leastone medical condition. A specific example representation will bedescribed in more detail below.

The method is typically performed at least in part using one or moreelectronic processing devices, for example forming part of one or moreprocessing systems, such as computers or servers, which could in turnconnected to one or more other computing devices, such as mobile phones,portable computers or the like, via a network architecture, as will bedescribed in more detail below.

In one example, the one or more electronic processing devices receivethe biomarker values, determine the indicator using biomarker values,determine the at least one control value using at least two of thebiomarker values, compare the at least one control value to therespective control value threshold and determine if the test is a validtest using the results of the comparison.

In this regard, the biomarker values can be received from a database orthe like, in which the values have been previously stored, or could bereceived directly from a measuring device, such as a PCR machine or thelike, which is used in determining the biomarker values. The processingdevices can then automatically assess the validity of the measurementsand then, if valid calculate the indicator, generating and displaying arepresentation of this as required. Thus, it will be appreciated thatthis can provide a substantially automated procedure from the point atwhich a sample is loaded into a measuring device.

In one example, the one or more electronic processing devices determinethe indicator by calculating a first indicator value using a ratio offirst and second biomarker values, calculating a second indicator valueusing a ratio of third and fourth second biomarker values anddetermining a sum of the first and second indicator values. The one ormore electronic processing devices similarly determine a plurality ofinternal relative control values by calculating a first control valueusing a ratio of the first and third biomarker values, calculating asecond control value using a ratio of the first and fourth biomarkervalues, calculating a third control value using a ratio of the secondand third biomarker values and calculating a fourth control value usinga ratio of the second and fourth biomarker values, before comparing eachcontrol value to a respective threshold range and displaying theindicator in response to a successful comparison for each control value.

When the biomarkers are gene expression products, the relative abundanceof target biomarkers can be determined thus; obtain a sample from abiological subject, such that the sample includes the target geneexpression products, then amplify at least the target gene expressionproducts in the sample, then for each gene expression product determinean amplification amount required to obtain a defined level of therespective gene expression product, the amplification amount beingdependent on the concentration of the gene expression product in thesample being based on a cycle time, number of amplification cycles, acycle threshold, an amplification time, or the like. In this case,relative biomarkers can be generated using combinations of amplificationtimes by subtracting amplification times for the respective geneexpression products so that these relative biomarker values represent aratio of the relative concentration of the respective gene expressionproducts.

It will be appreciated that the above described process is typicallyperformed on a biological subject presenting with clinical signs of atleast one medical condition. In this case, a medical practitioner willtypically perform an initial assessment of the clinical signs andestablish a specific test to be performed. For example, if thepractitioner identifies that the subject may have ipSIRS, the abovedescribed process is typically performed with relative biomarker valuescorresponding to relative concentrations of LAMP1, CEACAM4, PLAC8 andPLA2G7.

More typically the clinical signs could be common to first and secondconditions, which case the indicator is for use in distinguishingbetween the first and second conditions. Thus, for example, inSIRS andipSIRS typically have similar clinical signs, so practitioners can usethe indicator to distinguish between the conditions.

Thus, the above could be used for validating an indicator used indetermining the likelihood of a biological subject having at least onemedical condition, the biomarkers being quantified using aquantification technique of a selected type and the method including:

-   -   a) determining a plurality of biomarker values, each biomarker        value being indicative of a value measured or derived from at        least one corresponding measured biomarker of the biological        subject;    -   b) determining an indicator indicative of the likelihood of a        biological subject having at least one medical condition by:        -   i) calculating a first indicator value using first and            second biomarker values;        -   ii) calculating a second indicator value using third and            fourth second biomarker values;        -   iii) determining the indicator using the first and second            indicator values; and,    -   c) calculating at least one of:        -   i) a first control value using the first and third biomarker            values;        -   ii) a second control value using the first and fourth            biomarker values;        -   iii) a third control value using the second and third            biomarker values;        -   iv) a fourth control value using the second and fourth            biomarker values;    -   d) comparing the at least one control value to a respective        control value threshold; and,    -   e) selectively validating the indicator using the results of the        comparison.

Thus, the above could also be used for validating an indicator used indetermining the likelihood of a biological subject having at least onemedical condition, the biomarkers being quantified using aquantification technique of a selected type and the method including:

-   -   a) obtaining a sample from a biological subject, the sample        including gene expression products;    -   b) quantifying at least some gene expression products in the        sample to determine a concentration of the gene expression        product in the sample;    -   c) determining an indicator indicative of the likelihood of a        biological subject having at least one medical condition by        combining:        -   i) a first indicator value indicative of a ratio of the            concentration of the first and second gene expression            products; and,        -   ii) a second indicator value indicative of a ratio of the            concentration of the third and fourth gene expression            products; and,    -   d) determining control values by determining:        -   i) a first control value indicative of a ratio of the            concentration of the first and third gene expression            products;        -   ii) a second control value indicative of a ratio of the            concentration of the first and fourth gene expression            products;        -   iii) a third control value indicative of a ratio of the            concentration of the second and third gene expression            products; and,        -   iv) a fourth control value indicative of a ratio of the            concentration of the second and fourth gene expression            products;    -   e) comparing each control value to a respective control value        threshold; and,    -   f) selectively validate the indicator using the results of the        comparison.

In one example, the process is performed by one or more processingsystems operating as part of a distributed architecture, an example ofwhich will now be described with reference to FIG. 2.

In this example, a number of base stations 201 are coupled viacommunications networks, such as the Internet 202, and/or a number oflocal area networks (LANs) 204, to a number of client devices 203 andone or more measuring devices 205, such as PCR, sequencing machines, orthe like. It will be appreciated that the configuration of the networks202, 204 are for the purpose of example only, and in practice the basestations 201, client devices 203 and measuring devices 205, ancommunicate via any appropriate mechanism, such as via wired or wirelessconnections, including, but not limited to mobile networks, privatenetworks, such as an 802.11 networks, the Internet, LANs, WANs, or thelike, as well as via direct or point-to-point connections, such asBluetooth, or the like.

In one example, each base station 201 includes one or more processingsystems 210, each of which may be coupled to one or more databases 211.The base station 201 is adapted to be used in calculating and validatingindicators and generating representations for these to be displayed viaclient devices. The client devices 203 are typically adapted tocommunicate with the base station 201, allowing indicatorrepresentations to be displayed.

Whilst the base station 201 is a shown as a single entity, it will beappreciated that the base station 201 can be distributed over a numberof geographically separate locations, for example by using processingsystems 210 and/or databases 211 that are provided as part of a cloudbased environment. However, the above described arrangement is notessential and other suitable configurations could be used.

An example of a suitable processing system 210 is shown in FIG. 3. Inthis example, the processing system 210 includes at least onemicroprocessor 300, a memory 301, an optional input/output device 302,such as a keyboard and/or display, and an external interface 303,interconnected via a bus 304 as shown. In this example the externalinterface 303 can be utilised for connecting the processing system 210to peripheral devices, such as the communications networks 202, 204,databases 211, other storage devices, or the like. Although a singleexternal interface 303 is shown, this is for the purpose of exampleonly, and in practice multiple interfaces using various methods (e.g.,Ethernet, serial, USB, wireless or the like) may be provided.

In use, the microprocessor 300 executes instructions in the form ofapplications software stored in the memory 301 to allow the requiredprocesses to be performed. The applications software may include one ormore software modules, and may be executed in a suitable executionenvironment, such as an operating system environment, or the like.

Accordingly, it will be appreciated that the processing system 210 maybe formed from any suitable processing system, such as a suitablyprogrammed client device, PC, web server, network server, or the like.In one particular example, the processing system 210 is a standardprocessing system, which executes software applications stored onnon-volatile (e.g., hard disk) storage, although this is not essential.However, it will also be understood that the processing system could beany electronic processing device such as a microprocessor, microchipprocessor, logic gate configuration, firmware optionally associated withimplementing logic such as an FPGA (Field Programmable Gate Array), orany other electronic device, system or arrangement.

As shown in FIG. 4, in one example, the client device 203 includes atleast one microprocessor 400, a memory 401, an input/output device 402,such as a keyboard and/or display, and an external interface 403,interconnected via a bus 404 as shown. In this example the externalinterface 403 can be utilised for connecting the client device 203 toperipheral devices, such as the communications networks 202, 204,databases, other storage devices, or the like. Although a singleexternal interface 403 is shown, this is for the purpose of exampleonly, and in practice multiple interfaces using various methods (e.g.,Ethernet, serial, USB, wireless or the like) may be provided.

In use, the microprocessor 400 executes instructions in the form ofapplications software stored in the memory 401 to allow communicationwith the base station 201, for example to allow for selection ofparameter values and viewing of representations, or the like.

Accordingly, it will be appreciated that the client devices 203 may beformed from any suitable processing system, such as a suitablyprogrammed PC, Internet terminal, laptop, or hand-held PC, and in onepreferred example is either a tablet, or smart phone, or the like. Thus,in one example, the processing system 210 is a standard processingsystem, which executes software applications stored on non-volatile(e.g., hard disk) storage, although this is not essential. However, itwill also be understood that the client devices 203 can be anyelectronic processing device such as a microprocessor, microchipprocessor, logic gate configuration, firmware optionally associated withimplementing logic such as an FPGA (Field Programmable Gate Array), orany other electronic device, system or arrangement.

Examples of the processes for determining and validating measurements ofindicators will now be described in further detail. For the purpose ofthese examples it is assumed that one or more processing systems 210acts to receive measured biomarker values from the measuring devices,calculate indicator values and control values, and use these tocalculate and validate an indicator which can then be displayed as partof a representation via hosted webpages or an App residing on the clientdevice 203. The processing system 210 is therefore typically a serverwhich communicates with the client device 203 and measuring devices 205via a communications network, or the like, depending on the particularnetwork infrastructure available.

To achieve this the processing system 210 of the base station 201typically executes applications software for performing requiredprocesses, with actions performed by the processing system 210 beingperformed by the processor 300 in accordance with instructions stored asapplications software in the memory 301 and/or input commands receivedfrom a user via the I/O device 302, or commands received from the clientdevice 203.

It will also be assumed that the user interacts with the processingsystem 210 via a GUI (Graphical User Interface), or the like presentedon the client device 203, and in one particular example via a browserapplication that displays webpages hosted by the base station 201, or anApp that displays data supplied by the processing system 210. Actionsperformed by the client device 203 are performed by the processor 400 inaccordance with instructions stored as applications software in thememory 401 and/or input commands received from a user via the I/O device402.

However, it will be appreciated that the above described configurationassumed for the purpose of the following examples is not essential, andnumerous other configurations may be used. It will also be appreciatedthat the partitioning of functionality between the client devices 203,and the base station 201 may vary, depending on the particularimplementation.

An example process for establishing control and indicator referenceswill now be described in more detail with reference to FIG. 5.

In this example, at step 500 the processing system 210 determinesreference data in the form of biomarker values obtained for a referencepopulation.

A reference population is any population of interest for whichinformation is collected against which reference can be made. Forexample the population may be characterized into those with or without acondition, or with varying degrees of severity, prognosis, stage, orsimilar disease or condition stratification method.

The reference data may be acquired in any appropriate manner buttypically this involves obtaining gene expression product data from aplurality of individuals, selected to include individuals diagnosed withone or more conditions of interest, as well as healthy individuals. Theterms “expression” or “gene expression” refer to production of RNA onlyor production of RNA and translation of RNA into proteins orpolypeptides. In specific embodiments, the terms “expression” or “geneexpression” refer to production of messenger RNA (mRNA), ribosomal RNA(rRNA), microRNA (miRNA) or other RNA classes such mitochondrial RNA(mtRNA), non-coding RNA (ncRNA, lncRNA (long)), small interfering RNA(siRNA), transfer RNA (tRNA) or proteins.

As used herein, the terms “microRNA” or “miRNA” refer to a shortribonucleic acid (RNA) approximately 18-30 nucleotides in length(suitably 18-24 nucleotides, typically 21-23 nucleotides in length) thatregulates a target messenger RNA (mRNA) transcriptpost-transcriptionally through binding to the complementary sequences onthe target mRNA and results in the degradation of the target mRNA. Theterms also encompass the precursor (unprocessed) or mature (processed)RNA transcript from a miRNA gene. The conversion of precursor miRNA tomature miRNA is aided by RNAse such as Dicer, Argonaut, or RNAse III.

The conditions captured in the reference data are typically medical,veterinary or other health status conditions and may include anyillness, disease, stages of disease, disease subtypes, severities ofdisease, diseases of varying prognoses, or the like.

Example reference biomarkers could include expression products such asnucleic acid or proteinaceous molecules, as well as other moleculesrelevant in making a clinical assessment.

The individuals in the reference population also typically undergo aclinical assessment allowing any conditions to be clinically identifiedas part of the characterization process for the reference population,and with an indication of any assessment or condition forming part ofthe reference data. Whilst any conditions can be assessed, in oneexample the process is utilized specifically to identify conditions suchas SIRS (Systemic Inflammatory Response Syndrome) (M S Rangel-Frausto, DPittet, M Costigan, T Hwang, C S Davis, and R P Wenzel, “The NaturalHistory of the Systemic Inflammatory Response Syndrome (SIRS). aProspective Study.,” JAMA: the Journal of the American MedicalAssociation 273, no. 2 (Jan. 11, 1995): 117-123.). SIRS is anoverwhelming whole body reaction that may have an infectious ornon-infectious aetiology, whereas sepsis is SIRS that occurs duringinfection. Both are defined by a number of non-specific host responseparameters including changes in heart and respiratory rate, bodytemperature and white cell counts (Mitchell M Levy et al., “2001SCCM/ESICM/ACCP/ATS/SIS International Sepsis Definitions Conference,”Critical Care Medicine 31, no. 4 (April 2003): 1250-1256.; K Reinhart, MBauer, N C Riedemann, and C S Hartog, “New Approaches to Sepsis:Molecular Diagnostics and Biomarkers,” Clinical Microbiology Reviews 25,no. 4 (Oct. 3, 2012): 609-634). To differentiate these conditions theyare referred herein to as SIRS (both conditions), infection-negativeSIRS (SIRS without infection, hereafter referred to as “inSIRS”) andinfection-positive SIRS (sepsis, SIRS with a known or suspectedinfection, hereafter referred to as “ipSIRS”). The causes of SIRS aremultiple and varied and can include, but are not limited to, trauma,burns, pancreatitis, endotoxaemia, surgery, adverse drug reactions, andinfections (local and systemic). It will be appreciated from thefollowing, however, that this can be applied to a range of differentconditions, and reference to inSIRS or ipSIRS is not intended to belimiting.

Additional reference data may also be collected for the referencepopulation and may include additional biomarkers such as one or morephenotypic or clinical parameters of the individuals and/or theirrelatives that has not been generated or captured by instrumentmeasurements or a clinical assessment. Phenotypic parameters can includeinformation such as the gender, ethnicity, age, hair colour, eye colour,height, weight, waist and hip circumference, or the like. Also, in thecase of the technology being applied to individuals other than humans,this can also include information such as designation of a species,breed or the like. Clinical traits may include genetic information,white blood cell count, diastolic blood pressure and systolic bloodpressure, bone density, body-mass index, presence of diabetes or not,resting heart rate, HOMA (homeostasis model assessment), HOMA-IR(homeostasis model assessment insulin resistance), IVGT (intravenousglucose tolerance test), resting heart rate, 3 cell function,macrovascular function, microvascular function, atherogenic index,low-density lipoprotein/high-density lipoprotein ratio, intima-mediathickness, body temperature, Sequential Organ Failure Score (SOFA) andthe like.

The reference population has two functions, the first is to characterizepatients with respect to the condition of interest, in this examplecategorize patients into inSIRS and ipSIRS. The second is to capture thevalues required to generate values used in the assay. Thus, for areference population and for a specific indicator, application of theindicator to the reference data will produce a reference indicatordistribution of values corresponding to known categories or degrees suchas inSIRS and ipSIRS, against which indicator values determined from newsamples can be compared.

Similarly, internal relative controls can be generated using thereference population data and compared to internal relative controlssimilarly generated for each new sample.

Each individual within the reference population is typically allocatedto a group. The groups may be defined in any appropriate manner such asany one or more of an indication of a presence, absence, degree, stage,severity, prognosis or progression of a condition, other tests orassays, or measured biomarkers associated with the individuals.

For example, a first selection of groups may be used to identify one ormore groups of individuals suffering from SIRS, one or more groups ofindividuals suffering ipSIRS, and one or more groups of individualssuffering inSIRS. Further groups may also be defined for individualssuffering from other conditions. The groups may include overlappinggroups, so for example it may be desirable to define groups of healthyindividuals and individuals having SIRS, with further being defined todistinguish inSIRS patients from ipSIRS patients, as well as differentdegrees of inSIRS or ipSIRS, with these groups having SIRS in common,but each group of patients differing in whether a clinician hasdetermined the presence of an infection or not. Additionally, furthersubdivision may be performed based on phenotypic traits, so groups couldbe defined based on gender, ethnicity or the like so that a plurality ofgroups of individuals suffering from a condition are defined, with eachgroup relating to a different phenotypic trait.

It will also be appreciated, however, that identification of differentgroups can be performed in other manners, for example on the basis ofparticular activities or properties of biomarkers within the biologicalsamples of the reference individuals and accordingly, reference toconditions is not intended to be limiting and other information may beused as required.

The manner in which classification of patients in the referencepopulation into groups is performed may vary depending on the preferredimplementation. In one example, this can be performed automatically bythe processing system 201, for example, using unsupervised methods suchas Principal Components Analysis (PCA), or supervised methods such ask-means or Self Organising Map (SOM). Alternatively, this may beperformed manually by an operator by allowing the operator to reviewreference data presented on a Graphical User Interface (GUI), and definerespective groups using appropriate input commands.

Accordingly, in one example the reference data can include for each ofthe reference individuals information relating to at least one anddesirably to a plurality of reference biomarkers and a presence,absence, degree or progression of a condition.

The reference data may be collected from individuals presenting at amedical centre with clinical signs relating to any relevant conditionsof interest, and may involve follow-on consultations in order to confirmclinical assessments, as well as to identify changes in biomarkers,and/or clinical signs, and/or severity of clinical signs, over a periodof time. In this latter case, the reference data can include time seriesdata indicative of the progression of a condition, and/or the activityof the reference biomarkers, so that the reference data for anindividual can be used to determine if the condition of the individualis improving, worsening or static. It will also be appreciated that thereference biomarkers are preferably substantially similar for theindividuals within the sample population, so that comparisons ofmeasured activities between individuals can be made.

This reference data could also be collected from a single individualover time, for example as a condition within the individual progresses,although more typically it would be obtained from multiple individualseach of which has a different stage of the one or more conditions ofinterest.

It will be appreciated that once collected, the reference data can bestored in the database 211 allowing this to be subsequently retrieved bythe processing system 210 for subsequent analysis, or could be provideddirectly to the processing system 210 for analysis.

In one example, the measurements are received as raw data, which thenundergoes preliminary processing. Such raw data corresponds toinformation that has come from a source without modification, such asoutputs from instruments such as PCR machines, array (e.g., microarray)scanners, sequencing machines, clinical notes or any other biochemical,biological, observational data, or the like. This step can be used toconvert the raw data into a format that is better suited to analysis. Inone example this is performed in order to normalise the raw data andthereby assist in ensuring the biomarker values demonstrate consistencyeven when measured using different techniques, different equipment, orthe like. Thus, the goal of normalisation is to remove the variationwithin the samples that is not directly attributable to the specificanalysis under consideration. For example, to remove variances caused bydifferences in sample processing at different sites. Examples ofnormalisation that are well known in the art include z-scoretransformation for generic data, or popular domain specificnormalisations, such as RMA normalisation for microarrays.

However, it will also be appreciated that in some applications, such asa single sample experiment run on a single data acquisition machine,this step may not strictly be necessary, in which case the function canbe a Null function producing an output identical to the input.

In one example, the preferred approach for generating reference data isa paired function approach over log normalised data. Log normalisationis a standard data transformation on gene and protein expression data,because the measured biomarkers follow a log-normal distribution asdirectly measured by the instrument. Applying a log transform turns thedata into process-friendly normally distributed data. The biomarkervalues measured will depend on the predominant condition that is beingassessed so, for example, in the case of determining the likelihood of asubject having ipSIRS as opposed to inSIRS, the RNA biomarkers Bm₁, Bm₂,Bm₃, Bm₄ used could be LAMP1, CEACAM4, PLAC8 and PLA2G7. A secondpossible example, in the case of determining the likelihood of a subjecthaving liver disease, the protein biomarkers Bm₁, Bm₂, Bm₃, Bm₄ usedcould be Alkaline Phosphatase (AP), Aminotransferase (AT), AspartateAminotransferase (AspAT) and Gamma-glutamyl transpeptidase (GGT).

As part of the above process, at step 510 the measurements are validatedusing traditional prior art techniques, to ensure that the measurementshave been performed successfully, and hence are valid.

At step 520 at least four internal relative control values Ctrl₁, Ctrl₂,Ctrl₃, Ctrl₄, are determined for the reference population, with twoadditional control values Ctrl₅, Ctrl₆, being optionally determined asfollows:

Ctrl₁=(Bm₁/Bm₃)

Ctrl₂=(Bm₁/Bm₄)

Ctrl₃=(Bm₂/Bm₃)

Ctrl₄=(Bm₂/Bm₄)

Ctrl₅=(Bm₁/Bm₂)

Ctrl₆=(Bm₃/Bm₄)

At step 530, the control values used to update or create respectivecontrol reference data. In this regard, in the current example, eachcontrol reference is in the form of a distribution of control values forthe reference population including healthy individuals and individualssuffering from the conditions of interest. The distribution itself canbe used as a control reference, or alternatively one or more valuescould be derived therefrom, such as to define a threshold range. Forexample this could be set to encompass 99% of the distribution.

Additionally, the control reference could be defined so that it isspecific to characteristics of the individuals, such as the sex,ethnicity, age, weight, height or other physical characteristic of thesubject, thereby allowing different control references to be defined fordifferent groups of individuals with similar characteristics.

Once created the control references and in particular the controldistributions, are stored in the database 211 for subsequent use.

At step 540, first and second indicator values are determined. The firstand second indicator values In₁, In₂ are determined on a basis of ratiosof first and second, and third and fourth biomarker values respectively:

In₁=(Bm₁/Bm₂)

In₂=(Bm₃/Bm₄)

The indicator values used to update or create a set of indicatorreferences at step 550, which is used in analysing measured indicatorvalues for a subject to establish a likelihood of the subject having acondition. In particular, indicator values for each reference group arestatistically analysed to establish a range or distribution of indicatorvalues that is indicative of each group, thereby allowing the indicatorvalues to be used to discriminate between the different groups, andhence ascertain the likelihood a subject is suffering from a particularcondition, as will be described in more detail below.

An example process of a process for validating measurement of biomarkervalues used in generating an indicator will now be described in moredetails with reference to FIG. 6.

In this example, at step 600 values of four biomarkers Bm₁, Bm₂, Bm₃,Bm₄ are measured by the measuring device 205. The four biomarker valuesselected will depend on the predominant condition that is beingassessed. For example, in the case of determining the likelihood of apatient having ipSIRS as opposed to inSIRS, the biomarkers Bm₁, Bm₂,Bm₃, Bm₄ used will be LAMP1, CEACAM4, PLAC8 and PLA2G7.

At step 610 the processing system 210 determines first and secondindicator values, either directly from the measuring device 205, or byretrieving the values after storage in a database 211 or other datastore. The first and second indicator values In₁, In₂ are determined ona basis of ratios of first and second, and third and fourth biomarkervalues respectively:

In₁=(Bm₁/Bm₂)

In₂=(Bm₃/Bm₄)

At step 620 the processing device 210 combines the indicator values todetermine an indicator In which may be achieved utilising a sum of thefirst and second indicator values or other similar measure. So forexample:

In=In₁+In₂(Bm₁/Bm₂)+(Bm₃Bm₄)

At step 630, the processing device 210 determines the four controlvalues Ctrl₁, Ctrl₂, Ctrl₃, Ctrl₄, and optionally additional controlvalues Ctrl₅, Ctrl₆, as follows:

Ctrl₁=(Bm₁/Bm₃)

Ctrl₂=(Bm₁/Bm₄)

Ctrl₃=(Bm₂/Bm₃)

Ctrl₄=(Bm₂/Bm₄)

Ctrl₅=(Bm₁/Bm₂)

Ctrl₆=(Bm₃/Bm₄)

Thus, as shown in FIG. 6, each possible ratio of the values of the fourbiomarkers Bm₁, Bm₂, Bm₃, Bm₄ is calculated, with two of the ratiosbeing used to form the indicator values and hence the indicator, and allof the ratios being used for control values.

Each of the control values is then compared to a respective controlreference, and in particular control distribution, at step 640. In thisregard, it will be appreciated that the processing system 210 willretrieve respective control distributions for the particular biomarkersthat are used to determine the respective control values Ctrl₁, Ctrl₂,Ctrl₃, Ctrl₄, optionally additional control values Ctrl₅, Ctrl₆ withthese control distributions being previously determined and stored inthe database 211, as described above. At step 650, the processing system210 determines if each control value is acceptable based on the resultsof the comparison. In this regard, if any one control value is outsidethe defined control value threshold range, then this is indicative of atest failure which is communicated to the user at step 660, for exampleby providing an indication on a client device 203 of a medicalpractitioner requesting the test. Otherwise a representation of theindicator is displayed at step 670 on the client device 203, as will bedescribed in more detail below.

In the above described process, the values of the four biomarkers Bm₁,Bm₂, Bm₃, Bm₄ are used to determine four (or optionally six) controlvalues. Because each biomarker (Bm₁, Bm₂, Bm₃, Bm₄) is involved inmultiple comparisons with other biomarkers as controls (Ctrl₁, Ctrl₂,Ctrl₃, Ctrl₄), there are more opportunities for detection of an invalidunderlying biomarker than if each biomarker was measured against only asingle expected range or against a single control biomarker. Thismultiple testing of each biomarker results in far greater sensitivitythan would be achieved with individual comparison to an independentcontrol, as shown in the arrangement of FIG. 7B.

For example, in the case of FIG. 7B, representing a standard controlstrategy based on measured biomarkers, Bm₁ is measured relative to thecontrol, or equivalently to an absolute value using the control toderive the value. The measured value being outside an expected range isonly seen once in every thousand samples, so if the measured value fallsoutside of this range, the probability that this sample measurementbelongs to the distribution (that is, it is valid) is therefore 1/1000and p value for failure detection: 0.001.

In contrast in the case of the current system of FIG. 7A, the measuredvalue of biomarker Bm₁ is compared to each of biomarkers Bm₂, Bm₃, Bm₄.Each represents an independent check on the measured value of Bm₁, sovalues outside the range are only once every 1000 samples for eachcomparison. The probability that this sample is not in the distributionof valid samples (a failure) is 1/1000× 1/1000× 1/1000, so that the pvalue for failure detection: 1e-9, meaning this relative controlcombination shown in FIG. 7A is a million times more sensitive than astandard measured control shown in FIG. 7B.

A further example will now be described with reference to FIGS. 8A and8B.

In this example, at step 800 a sample is acquired from the subject. Thesample could be any suitable sample such as a peripheral blood sample,or the like, depending on the nature of the biomarker values beingdetermined. At step 805 the sample undergoes preparation allowing thisto be provided to the measuring device 205 and used in a quantificationprocess at step 810. For the purpose of this example, the quantificationprocess involves PCR amplification, with the measuring device being aPCR machine, although other suitable biomarker measurement devices andtechniques could be used. In this instance, amplifications times At₁,At₂, At₃, At₄, are determined for each of the four biomarkers Bm₁, Bm₂,Bm₃, Bm₄ at step 815, with the amplification times being transferredfrom the measuring device 205 to the processing system 210 allowing theprocessing system 210 to perform analysis of the corresponding biomarkervalues.

Accordingly, at step 820 the processing system 210 calculates ratiosusing the amplifications times. In this regard, as the amplificationtimes represent a log value, the ratios are determined by subtractingamplifications times as will be appreciated by a person skilled in theart.

Accordingly, in this example the indicator and control values would bedetermined as follows:

Ctrl₁=(Log Bm₁−Log Bm₃)=(At₁−At₃)

Ctrl₂=(Log Bm₁−Log Bm₄)=(At₁−At₄)

Ctrl₃=(Log Bm₂−Log Bm₃)=(At₂−At₃)

Ctrl₄=(Log Bm₂−Log Bm₄)=(At₂−At₄)

As previously mentioned, the indicator values can also be used ascontrol values, leading to two further control valves:

Ctrl₅=(Log Bm₁−Log Bm₂)=(At₁−At₂)

Ctrl₆=(Log Bm₃−Log Bm₄)=(At₃−At₄)

The processing system compares the ratios representing the controlvalues to respective control value threshold ranges retrieved from thedatabase 211, at step 825. Again, this can be based on characteristicsof the subject, with the control values being derived from controlvalues measured for a sample population of individuals with similarcharacteristics.

At step 830, the processing system 210 determines if the control ratioscorrespond to control values that are acceptable, in other words if theyfall within the defined threshold range. If this is not the case thentest failure is indicated, for example, by having the processing system210 generate a failure notification and provide this to a client device205 at step 835. The notification could be of any suitable form andcould include an email, notification in a dash board of a testmanagement software application or the like. As part of this, anyoutlier ratios that fall outside the control value ranges can beidentified, allow an operator to identify which if any of the biomarkervalues failed or was inaccurately measured for any reason.

In the event that each of the control values are acceptable, at step 840the processing system 210 determines an indicator value by combining theratios for the indicator values, as follows:

In=(Log Bm₁−Log Bm₂)+(Log Bm₃−Log Bm₄)=(At₁−At₂)+(At₃−At₄)

The processing system 210 then compares the indicator value to one ormore respective indicator thresholds at step 845.

As previously described, the indicator references are derived for asample population and are used to indicate the likelihood of a subjectsuffering from ipSIRS or another condition. To achieve this, theindicator reference is typically derived from a sample population havingsimilar characteristics to the subject. The sample population istypically grouped based on a clinical assessment into groups having/nothaving the conditions or a measure of severity, risk or progressionstage of the condition, with this then being used to assess thresholdindicator values that can distinguish between the groups or provide ameasure of severity, risk or progression stage. The results of thiscomparison are used by the processing system 210 to calculate alikelihood of the subject having ipSIRS at step 850, with this beingused to generate a representation of the results at step 855, which istransferred to the client device 203 for display at step 860, forexample as part of an email, dashboard indication or the like.

An example of the representation is shown in FIGS. 9A and 9B.

In this example, the representation 900 includes a pointer 910 thatmoves relative to a linear scale 930. The linear scale is divided intoregions 921, 922, 923, 924 which indicates the probability of a subjecthaving either SIRS or sepsis. Corresponding indicator number values aredisplayed at step 930 with an indication of whether the correspondingvalue represents a likelihood of inSIRS or ipSIRS being showing at step940. An alphanumeric indication of the score is shown at step 951together with an associated probability of the biological subject havingipSIRS at step 952.

As shown in this example, regions of the linear scale where the pointeris situated are highlighted with the diagnosis that is most unlikelybeing greyed out to make it absolutely clear where the subject sits onthe scale. This results in a representation which when displayed at step860 is easy for a clinician to readily understand and to make a rapiddiagnosis.

Features of the benefits of using derived internal controls over priorart will now be described with reference to FIGS. 10A, 10B and 10C.

Using the above example with four measured biomarkers used in generatingthe indicator value, a standard control methodology is shown in FIG.10A. The work flow of the assay is divided up into physical 1010 andalgorithmic 1020 components, where the physical components 1010 areinherent hardware, reagents and non-software components and thealgorithmic components 1020 are carried out on an appropriate computingdevice 210. The values of the measured biomarkers 1015 and externalmeasured controls 1030 are generated using the physical device 1010.External positive controls 1030 are used to validate that the test isable to produce a result in the reportable range by measuring against areference range 1040, and if no, then fail the test 1050, or if yes,output the indicator value 1060 in a report 1070.

For comparison, the same device using internal relative controls isshown in FIG. 10B. Again the workflow is divided up into physical 1010and algorithmic 1020 components and the measured biomarkers 1015 aregenerated on the physical device 1010. Note that the external positivecontrols 1030 shown in FIG. 10A are not present, and there is anadditional generation of internal relative controls 1035 which arederived from the measured biomarkers 1015. Otherwise the two methods aresimilar with the testing of controls against reference thresholds 1040,and if outside these thresholds a failure of the assay will be reported1050, else the indicator value 1060 will be output in the form of areport 1070.

It will be appreciated that by removing physical components of a devicein the form of external controls 1030, and replacing their function withthe use of internal relative controls 1035, the control component of thetest has been shifted from a physical component, with fixed costs perunit, to the algorithmic component, which is substantially more scalableas software. Therefore the use of internal relative controls as shown inFIG. 10B has advantages over the use of prior art measured controls FIG.10A including lower cost, complexity and risk for industrial manufactureor processing risk.

An extension of this method is example in FIG. 10C, which may be used inthe event that the measured biomarkers 1015 are unable or impractical toproduce relative controls 1035 that completely cover all instances wherea failed sample must be detected 1040. The use of such additionalinternal relative controls is useful in the case where an indicatorvalue may be invalid even if no combinations of controls comprising theindicator may be out of range. An example may be that a test is designedfor use in people with an inflammatory response due to infection, andthe indicator value may be a measure of the likelihood that the patientwill improve or deteriorate in the following 24 hours. It may be thatthe indicator is highly prognostic in this population, and that aninternal relative biomarker not related to this indicator can reliablydistinguish those with and without infection. In this example theinternal relative biomarker can be used as an additional control toensure that the patient is indeed infected, and therefore part of theintended use population, and therefore that the indicator value is validfor use on this patient. In this case an additional measured biomarker1031 can be run on the physical device 1010 in parallel with themeasured biomarkers 1015 that are used for the indicator value 1060.Additional measured biomarkers 1031 need not be used in the indicatorvalue (test result), and are specifically selected to ensure that anyinvalid samples, when tested for acceptable ranges 1040 willappropriately fail the test 1050. It will be appreciated that multipleadditional internal controls 1031 may be required to cover all possiblecases of invalid samples and that a plurality of functions may beapplied to additional measured internal controls in combination witheach other and the measured biomarkers comprising the indicator value tomeet this goal. It will further be appreciated that generally it issimpler and cheaper to measure additional internal controls 1031 in amedical device than to use external controls 1030, and therefore eventhe addition of many internal controls will generally still provide costand complexity advantages over the use of a small number of externalcontrols.

An example will now be described with reference to FIGS. 12A to 12C(a)-(f), which show kernel density plots for this population. Thedistribution of Cycle Threshold (Ct) values for each gene is shown insolid lines, and the Ct values for synthesized In-Vitro Transcripts atknown concentrations as controls is shown as dashed lines.

Another advantage of the use of relative controls method over standardmeasured controls will be described with reference to FIGS. 11A and 11B,using the same example. In FIG. 11A, the Ct value for each of themeasured biomarkers (Bm₁, Bm₁, Bm₃, Bm₄) is shown over a range ofconcentrations ranging from 20 to 2000 ng input to the PCR reaction. Inthis example the indicator value is measured as a sum of proportions ofthese measured biomarkers:

In=In₁+In₂=(Bm₁/Bm₂)+(Bm₃/Bm₄)

The indicator value for these biomarkers at each concentration is shownin FIG. 11B, which shows a flat profile despite very large changes inconcentration over the input range. These results demonstrate that theindicator is stable over a range of input concentrations because itmeasures the relative concentrations between the biomarkers. If areference threshold (in Ct units) were applied over the stable indicatorrange (20 to 2000 ng input) to each of the measured biomarkers in FIG.11A, then the reference ranges would be very wide. In this example,valid ranges for PLAC8 would range from 17 to 23 Ct units. Such widecontrol reference ranges are generally not appropriate, so typicallyassays specify a narrow input range so that reference ranges of measuredbiomarkers can be made sufficiently narrow. This step in creating aninput concentration from a sample is an extra step in processing thatcan be removed if the indicator value uses relative information derivedfrom measured biomarkers, and if the controls also use relativeinformation derived from measured biomarkers. Such an example showsimproved utility over controls using measured biomarkers in this case bythe removal of a processing step requiring diluting to a specified inputconcentration, which saves time, cost and reduces complexity.

In this example, the data shown in FIG. 12A is taken from 106consecutive samples with 2 or more ipSIRS criteria, divided in patientswith or without infection, for biomarkers in the form of signature genesLAMP1, CEACAM4, PLAC8 and PLA2G7.

Next a failure of the measurement of one of the four signature geneswill be described. Using sample number 13, and artificially reducingreaction efficiency of the LAMP1 reaction to 89% (failed assay) wereduce the recorded Ct value from 25.71 to 22.88. The probability basedon reference Ct observations for this gene that the assay has failed is32.5% as shown for LAMP1 in FIG. 12B (a)-(f), not sufficient to declarea failed assay. FIG. 12B (a)-(f) show the Ct values recorded for each ofthe other biomarkers (PLA2G7, PLAC8 and CEACAM4) for the failed sample,and also that for both the high and low positive controls, the valuesare in the expected ranges.

Table 2 shows that for each of the measured biomarkers and both controlsthat the values are within the reference ranges and there is notsufficient evidence (p<0.05) to identify the failed sample.

TABLE 2 PLA2G7 PLAC8 CEACAM4 LAMP1 Hi Pos Cntl Low Pos Cntl Sample Value30 22 24 25.5 21 33 In range Yes Yes Yes Yes Yes Yes P value 1.000 0.2801.000 0.325 1.00 1.00

Now looking at ratios between the measured biomarkers in FIG. 12C(a)-(f), the ratio PLAC8-LAMP1 clearly detects the failure p<0.001,which is sufficient to correctly declare the assay failed.

Table 3 shows that the failed assay is detected by a low p value (<0.01)for the relative values of PLAC8 to LAMP1.

TABLE 3 PLA2G7 − PLA2G7 − PLA2G7 − PLAC8 − PLAC8 − CEACAM4 − PLAC8CEACAM4 LAMP1 CEACAM4 LAMP1 LAMP1 Sample Value 8 6 4.5 −2 −3.5 1.5 Inrange Yes Yes Yes Yes No Yes P value 0.204 0.927 0.291 0.133 <0.0010.133

FIG. 12D plots the failed sample as a scatterplot of PLAC8 and LAMP1. Itcan be seen that although this sample is within the maximum and minimumranges of both PLAC8 and LAMP1, it is a clear outlier when consideringthe relative position with respect to each of the these biomarkers. Thisis reflected in the low p value for this relative control, as it isoutside of the reference range and thus is correctly identified as afailed sample.

Accordingly, using this approach, multiple relative controls that maynot individually be sufficient to declare a failed assay can be combinedusing a Bayes Rule or other probabilistic method to give a jointprobability of failure.

Accordingly, the above process described the use of controls comprisedof ratios of measured biomarker values, such as expression of targetgenes, rather than the use of non-target internal or external controlsor spike-ins, in gene expression experiments and analyses. Advantages ofsuch an approach include no additional measurements beyond those usedfor the indicator values, the ability to analyse more targets in asingle experiment and reduced overall costs of performing geneexpression analysis in addition to higher sensitivity and the ability toskip an input normalizing step during processing if the indicator valuesare also comprised of ratios.

An example will now be described with reference to FIG. 13A (a)-(d)which shows data from BUPA Medical Research Ltd in a study of malepatients for the detection of liver damage due to alcoholism. In thisstudy there were 144 enrolled volunteers classified as alcoholic, and200 enrolled volunteers classified as non-alcoholic. Measurements ofliver related proteins from peripheral blood were taken for eachvolunteer and a diagnostic combination of these proteins (the indicator)for alcohol related liver damage yielded 94% accuracy of classification(Comak, Emre, et al. “A new medical decision making system: Least squaresupport vector machine (LSSVM) with Fuzzy Weighting Pre-processing.”Expert Systems with Applications 32.2 (2007): 409-414.). The utility ofthis test is that an accurate diagnosis of liver damage due toalcoholism can be made with a multi-marker protein panel from peripheralblood in preference to an invasive and more expensive liver biopsy. Fourproteins were measured from peripheral blood; Alkaline Phosphatase (AP),Aminotransferase (AT), Aspartate Aminotransferase (AspAT) andGamma-glutamyl transpeptidase (GGT). The reference population is the setof patients in the study, and the reference data are the measured valuesfor each of the protein concentrations plus the relative controlsdefined as all pair-wise ratios of abundance of each protein. In thisexample, consider a hypothetical new sample for a patient with unknownliver status with the following measurements AP=56, AT=49, AspAT=28 andGGT=6. An indicator value for liver disease may be generated from thesemeasurements, but it is not yet known if the values are valid (if therehas been some failure in measurement). All of these measurements arewithin the observed range of values for each of these proteins, so thissample would traditionally be considered as having satisfied thecontrols (being within the observed reference distribution. FIG. 13A(a)-(d) shows the measured values for the new sample against thereference distribution for each measured biomarker (AP, AT, AspAT andGGT)). It can be seen that the sample falls within the reference rangefor each measured biomarker and therefore would traditionally beconsidered valid. Table 4 shows the values for each measured biomarkeragainst the reference distribution and the probability of failure foreach reference distribution.

TABLE 4 AP AT AspAT GGT Sample Value 56 49 28 6 In range Yes Yes Yes YesP value 0.451 0.340 0.739 0.411

FIG. 13B (a)-(f) show the same sample against derived controldistributions. In the case of AT-GGT and Asp-GGT, there are p valuesless than 0.05, indicating that this sample is unlikely to have comefrom the population of samples for which this test was designed, orthere is some other failure, and therefore the indicator value (result)for this test is invalid.

Table 5 shows the relative control values for this sample and thespecific controls capable of detecting this failure.

TABLE 5 AP − AP − AP − AT − AT − AspAT − AT AspAT GGT AspAT GGT GGTSample Value 7 28 50 21 43 22 In range Yes Yes Yes Yes No No P value0.208 0.381 0.653 0.157 0.023 0.006

Accordingly, the above described system introduces the use of relativeinternal controls in the case of multi-biomarker medical devices, suchthat the need for controls that are not internal relative controls maybe reduced or eliminated.

In one example, the relative internal controls are relative biomarkersinternal to the sample that are used to ensure that the values used inestablishing an indicator are valid. The relative biomarkers can bederived from measured biomarker values, with these being used bydefining corresponding acceptable reference thresholds for each relativebiomarker. These relative biomarkers may or may not include relativebiomarkers used in determining the indicator, and could include the samebiomarker values used in different or the same combinations. In oneexample, this provides a set of relevant controls without the need forany additional measured biomarkers being added to the assay.

The system can further be used to provide the appropriate use of thesecontrols in a medical device using the relative biomarkers used inestablishing the indicator value.

The system can also provide a method by which additional internalrelative biomarkers may be added to the group of relative controls tomeet any arbitrarily stringent control requirement such that a minimalset of additional measured biomarkers is required, thus providing anoptimal performance for a minimum cost.

Despite allowing additional markers to be avoided, the system cansuccessfully detect test failure in cases where prior art methods arenot able to, a critical advance in the case of medical devices whereacting on an invalid test results can have potentially life threateningconsequences.

The system is also shown by example to appropriately pass a result incases where prior art methods unnecessarily fail a sample. Also animportant advance for medical devices with potentially life-criticalconsequences if a test is unnecessarily failed (and the result istherefore unavailable).

A further example will now be described using in-house data derived fromthe use of real-time polymerase chain reaction (RT-PCR) on 546 bloodsamples taken from patients with suspected sepsis. The results of theassay provide a probability of a patient having sepsis (or SIRS) basedon a formula that uses the PCR Ct (cycle time) values for each of fourtarget genes (PLA2G7, PLAC8, LAMP1 and CEACAM4).

The method in brief was as follows. Patient blood was collected directlyinto PAXgene tubes and total RNA extracted. The RT-PCR assay wasprovided in kit form to a hospital laboratory based in the Netherlands.The assay uses quantitative, real-time determination of the amount ofeach four host immune cell RNA transcripts in the sample based on thedetection of fluorescence on a qRT-PCR instrument (e.g. AppliedBiosystems 7500 Fast Dx Real-Time PCR Instrument, Applied Biosystems,Foster City, Calif., catalogue number 440685; K082562). Transcripts areeach reverse-transcribed, amplified, detected, and quantified in aseparate reaction well for each target gene using a probe that wasvisualized in the FAM channel. Each of the four target genes has a knownCt range and when assay results are obtained outside of these ranges thetest is failed. For each sample the following internal controls werealso run in separate reaction vessels—HIGH, LOW, NEGATIVE and ano-template (NTC). The HIGH, LOW and NEGATIVE internal controls containa known quantity of an artificial DNA template—each of these separatereactions must also fall within a particular Ct range for the assay topass, and the NTC must not amplify a PCR product.

A summary table of the results from running the assay on these 546samples using both control methods (“Normal” and “Relative”) is shownbelow in Table 6. Full results are shown in Tables 7, 8, 9 and 10.

TABLE 6 Relative Normal Fail Pass Fail (Controls) 0 23 Fail (FourTargets) 2 3 Pass 13 505

A brief summary, explanation and discussion of these results follows.

505 samples (92.5%) were passed using both control methods.

Two (2) samples were failed using both control strategies. Using theNormal controls method both samples failed because the Ct values for thetarget gene PLA2G7 were out of the expected Ct range. Using the Relativecontrols method these same two samples were strongly failed becausemultiple Relative control p values were obtained that were less than0.001.

26 samples were failed using Normal controls method but were passedusing Relative control method. Of these 26 samples, 23 were failedbecause the LOW control was out of range. For these 23 samples, allindividual gene target measurements (PLA2G7, PLAC8, LAMP1 and CEACAM4)were within the expected Ct range, and all Relative controls passed.Upon further inspection of the Ct values for individual genes and otherNormal controls the 23 samples that were failed because of oneout-of-range Normal control (LOW) should not have been. The Relativecontrol strategy did not fail these samples. In practice this would meanthat the use of the Relative controls strategy would have ‘rescued’ 23valid diagnostic tests that would be denied to the patient using aNormal control method.

The other three (3) samples failed using the Normal method because theCt values for PLA2G7 were out of the expected range for this gene. Ofthese three samples:

-   -   1) Sample IXP_128: reported adjusted p values for the Relative        controls above 0.045. Such a p value suggests that this sample        should not have been failed. Upon further inspection of the Ct        values all individual gene measurements were comparatively high        (in the high range for values expected for the other three genes        but not out of range). Such a result suggests that the assay was        run with a low input RNA concentration or low quality RNA.        Despite this, a valid probability of sepsis was still able to be        calculated. Further, the retrospective clinical diagnosis of the        patient matched the sepsis probability from the assay, implying        that the assay result was valid and should not have been failed.    -   2) Sample 6869: The Reported Relative control p values were as        low as 0.0016. A result like this can be expected in 16 in every        thousand normal tests.    -   3) Sample 1357: The Reported Relative control p values were as        low as 0.002. A result like this can be expected in one in every        five hundred normal tests.

The use of Relative control p values allows a clinical interpretation ofthe relevance of an abnormality level (p value), rather than an absolutecall, allowing the treating physician (or a procedure on behalf of thephysician) to determine the optimal p value at which to call a failstatus on the test.

There were 13 instances where the Normal method passed samples whereasthe Relative method did not. In these instances all measured genemarkers were within the expected Ct range, and all Normal controls werealso within range. However, these samples resulted in a low p valueusing the Relative method. In fact, the probability that the Relativecontrols measurements would happen by chance for any of these samples isless than one in one thousand. These Relative control results suggests ahigh level of abnormality for these 13 samples, and implies that thesesamples are not similar to other samples observed, nor similar to thepatient population used for the development and interpretation of thediagnostic. Based on the high level of abnormality using the Relativecontrol approach these 13 samples should be failed despite the measuredmarkers and Normal controls falling within expected Ct range. In thisinstance the Relative control approach is especially useful, as it hasidentified patients for whom the interpretation of the diagnostic resultusing the Normal control approach is not valid. Further, the Relativecontrol approach provides a confidence of the non-validity of theresult. These latter two points are discussed in more detail below.

Considering sample 3787: all Ct values of the genes are within expectedrange, and the Normal internal controls all are within range. Thus, thisresult would be considered valid using the Normal control approach.However, the Relative control CEACAM4/LAMP1 has a p value of 0.0007642and the Relative control LAMP1/PLAC8 has a p value of 3.96E-07indicating that such a result occurs in less than one in a million cases(based on the distribution curve of expected results). Such a result canbe interpreted in two ways:

-   -   1). The test values are correct, and this really is a        one-in-a-million patient.    -   2). The test values are incorrect and there is some problem with        the assay.

Any patient sample that generates a test result so radically different(1:1,000,000 chance) from all other patient samples should not bediagnosed with reference to other patient sample results that fit withinthe normal distribution—such a result should at least be furtherinvestigated (e.g. repeat the assay and/or investigate patient clinicalnotes).

Thus, when Relative controls approach reveal highly unlikely results(based on p value) the test should be failed. In these 13 cases, throughappropriate failure of the samples, the Relative controls approachcan 1) ‘protect’ patients from diagnostic calls that are unlikely to beactually valid, and 2) detect more sensitively test results that do notreflect the true status of the patient.

Full results of the 546 assays are shown in Tables 7, 8, 9 and 10.

Table 7 shows raw data results for 505 samples (of 546) that passedusing both the Normal and Relative controls method.

TABLE 7 Normal Controls Relative Controls External CEACAM4/ CEACAM4/CEACAM4/ LAMP1/ LAMP1/ PLAC8/ Sample CEACAM4 LAMP1 PLA2G7 PLAC8 ControlsStatus LAMP1 pVal PLAC8 pVal PLA2G7 pVal PLAC8 pVal PLA2G7 pVal PLA2G7pVal Status 6607 21.3 23.7 25.0 21.6 P P 0.368 0.233 0.472 0.535 0.2220.185 P 6617 22.5 24.3 28.0 19.8 P P 0.769 0.267 0.838 0.163 0.941 0.405P 6629 21.6 24.0 27.3 21.1 P P 0.363 0.581 0.762 0.945 0.897 0.948 P6636 22.0 24.7 32.7 22.0 P P 0.205 0.331 0.003 0.906 0.012 0.049 P 664823.7 23.6 29.2 22.3 P P 0.072 0.916 0.810 0.233 0.248 0.787 P 6650 21.624.3 29.5 17.9 P P 0.224 0.059 0.146 0.004 0.341 0.018 P 6653 21.2 24.026.8 18.3 P P 0.183 0.193 0.809 0.019 0.685 0.328 P 6656 24.0 24.3 28.522.3 P P 0.148 0.737 0.786 0.491 0.668 0.971 P 6657 22.4 23.2 26.9 21.2P P 0.414 0.963 0.753 0.521 0.944 0.765 P 6658 22.0 24.3 31.1 21.4 P P0.480 0.672 0.036 0.953 0.058 0.120 P IPX-098 21.5 23.8 30.5 19.3 P P0.433 0.422 0.041 0.155 0.072 0.025 P IXP-080 23.9 24.4 25.2 20.0 P P0.232 0.042 0.050 0.191 0.133 0.627 P IXP-081 23.6 24.3 27.2 22.0 P P0.368 0.778 0.451 0.725 0.719 0.628 P IXP-082 23.2 25.2 28.5 21.9 P P0.623 0.984 0.921 0.706 0.890 0.922 P IXP-083 24.0 24.7 29.5 22.1 P P0.317 0.592 0.820 0.879 0.458 0.608 P IXP-087 23.1 23.7 26.4 21.1 P P0.265 0.595 0.363 0.810 0.674 0.638 P IXP-088 22.5 24.7 25.5 21.6 P P0.489 0.816 0.263 0.800 0.122 0.270 P IXP-089 21.2 23.1 28.8 20.4 P P0.705 0.738 0.188 0.935 0.221 0.348 P IXP-090 21.1 23.7 29.0 21.2 P P0.256 0.319 0.142 0.810 0.313 0.498 P IXP-095 22.6 25.2 27.4 18.7 P P0.246 0.043 0.870 0.003 0.450 0.292 P IXP-096 22.9 24.5 27.3 22.0 P P0.954 0.779 0.708 0.797 0.666 0.624 P IXP-097 24.0 25.0 28.8 21.9 P P0.566 0.508 0.911 0.771 0.868 0.768 P 6681 25.2 26.0 30.3 23.1 P P 0.3800.530 0.977 0.972 0.640 0.692 P 6694 21.4 24.3 27.3 21.7 P P 0.159 0.2480.669 0.831 0.808 0.754 P 6706 22.7 24.4 28.2 20.7 P P 0.896 0.559 0.8080.473 0.845 0.579 P 6709 21.5 24.5 26.9 21.3 P P 0.102 0.423 0.860 0.7430.532 0.748 P 6713 21.4 24.2 26.1 19.5 P P 0.174 0.596 0.844 0.121 0.3740.886 P 6718 23.2 24.3 29.7 19.6 P P 0.612 0.069 0.452 0.116 0.289 0.085P 6735 23.1 25.7 27.4 23.2 P P 0.247 0.311 0.696 0.808 0.319 0.349 P6744 20.5 23.7 31.1 19.1 P P 0.066 0.923 0.004 0.151 0.027 0.010 P 674621.1 23.2 26.1 18.2 P P 0.573 0.184 0.935 0.068 0.712 0.475 P 6750 22.323.6 26.1 21.0 P P 0.759 0.923 0.492 0.906 0.559 0.592 P 6888 23.4 24.526.3 22.2 P P 0.593 0.985 0.238 0.714 0.317 0.313 P IXP_099 23.0 24.625.5 21.2 P P 0.997 0.639 0.175 0.619 0.146 0.372 P IXP_102 24.8 26.228.2 22.8 P P 0.806 0.575 0.367 0.675 0.398 0.654 P IXP_104 24.4 25.728.9 22.5 P P 0.783 0.646 0.763 0.772 0.852 0.992 P IXP_105 22.4 24.228.1 19.6 P P 0.857 0.210 0.760 0.142 0.812 0.315 P IXP_107 22.5 23.625.2 21.3 P P 0.532 0.996 0.214 0.648 0.307 0.282 P IXP_108 23.2 25.127.3 22.2 P P 0.759 0.852 0.597 0.982 0.471 0.571 P IXP_110 22.1 23.929.4 19.5 P P 0.829 0.291 0.261 0.200 0.272 0.111 P IXP_111 22.2 24.427.1 21.6 P P 0.472 0.630 0.907 0.994 0.629 0.700 P IXP_112 24.2 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0.723 P 324795420.6 24.1 26.8 20.8 P P 0.031 0.281 0.578 0.681 0.633 0.876 P 304206024.6 26.9 27.6 22.8 P P 0.373 0.670 0.289 0.272 0.114 0.507 P 296118225.1 26.5 32.4 23.7 P P 0.859 0.896 0.258 0.992 0.192 0.292 P 314174921.0 23.0 24.0 20.0 P P 0.610 0.844 0.287 0.873 0.162 0.300 P 317439822.5 23.4 27.2 22.2 P P 0.466 0.492 0.842 0.207 0.881 0.563 P 242535221.3 23.2 24.4 20.0 P P 0.713 0.969 0.287 0.758 0.185 0.370 P 262833421.1 25.0 27.1 22.4 P P 0.009 0.054 0.642 0.870 0.421 0.461 P 215685722.4 23.5 27.7 20.7 P P 0.553 0.753 0.893 0.925 0.660 0.762 P 322278622.9 24.0 27.4 20.9 P P 0.622 0.586 0.770 0.824 0.946 0.945 P 300850223.1 24.4 28.1 19.3 P P 0.749 0.047 0.950 0.059 0.927 0.263 P 300716223.4 25.9 28.1 23.0 P P 0.335 0.536 0.845 0.969 0.490 0.593 P 317305724.3 24.8 31.1 21.7 P P 0.213 0.320 0.366 0.875 0.112 0.172 P 311144321.0 23.4 28.2 19.1 P P 0.353 0.625 0.265 0.233 0.463 0.211 P 267197924.0 25.8 29.6 22.7 P P 0.817 0.935 0.792 0.799 0.867 0.783 P 262790422.8 24.7 27.6 22.0 P P 0.686 0.703 0.900 0.909 0.737 0.739 P 278906122.2 25.3 29.6 24.1 P P 0.074 0.016 0.210 0.204 0.649 0.735 P 326986821.6 24.6 27.0 22.1 P P 0.118 0.197 0.865 0.806 0.550 0.538 P 303399622.4 24.5 26.8 21.4 P P 0.576 0.864 0.706 0.824 0.494 0.670 P 291098123.4 27.5 29.8 24.4 P P 0.005 0.095 0.503 0.815 0.505 0.684 P 301818322.0 24.3 28.9 21.1 P P 0.420 0.808 0.346 0.745 0.543 0.503 P 312514723.1 24.7 27.4 22.8 P P 0.981 0.485 0.687 0.467 0.657 0.447 P 312946821.0 24.5 28.4 22.1 P P 0.030 0.075 0.224 0.748 0.826 0.999 P 285760122.5 23.8 26.8 21.9 P P 0.784 0.616 0.682 0.463 0.762 0.515 P 263662325.4 27.0 31.6 24.4 P P 0.953 0.806 0.558 0.826 0.549 0.719 P 278102624.2 27.5 31.4 25.3 P P 0.045 0.073 0.260 0.641 0.833 0.929 P 325536023.1 24.7 27.4 21.5 P P 0.951 0.803 0.698 0.756 0.655 0.851 P 302965223.8 26.8 27.0 22.7 P P 0.115 0.966 0.313 0.273 0.062 0.370 P 291201325.7 27.1 33.5 25.4 P P 0.819 0.455 0.146 0.336 0.094 0.415 P 288047122.9 25.3 29.5 20.8 P P 0.419 0.497 0.442 0.190 0.673 0.286 P 315147322.3 23.1 25.3 20.5 P P 0.369 0.646 0.276 0.873 0.472 0.504 P 313124323.4 24.3 26.2 20.9 P P 0.444 0.328 0.234 0.625 0.371 0.653 P 281117422.3 26.2 27.9 21.9 P P 0.009 0.541 0.804 0.217 0.301 0.883 P 276872224.3 25.8 29.1 21.7 P P 0.953 0.324 0.895 0.314 0.911 0.639 P 260174822.3 23.1 27.1 20.2 P P 0.370 0.496 0.871 0.938 0.785 0.793 P 279313921.2 21.4 26.5 17.9 P P 0.103 0.114 0.900 0.615 0.343 0.297 P 175 22.823.6 28.7 18.0 P P 0.380 0.007 0.665 0.026 0.366 0.050 P 1528 23.9 24.229.4 20.6 P P 0.133 0.114 0.850 0.550 0.342 0.273 P 763 21.6 22.4 23.619.4 P P 0.402 0.467 0.104 0.866 0.185 0.330 P 2449113 23.7 25.2 28.522.7 P P 0.922 0.865 0.902 0.801 0.934 0.836 P 1239 23.7 25.9 27.6 23.1P P 0.508 0.631 0.530 0.974 0.315 0.409 P 2240 20.9 25.2 30.4 19.5 P P0.002 0.865 0.021 0.016 0.343 0.036 P 1894 23.3 25.5 27.6 23.2 P P 0.4750.390 0.691 0.690 0.434 0.395 P 1143 23.8 25.6 30.4 20.3 P P 0.855 0.0860.445 0.049 0.466 0.094 P 2888477 21.3 23.4 28.7 19.6 P P 0.591 0.6780.228 0.406 0.305 0.199 P 2786127 22.2 23.7 27.0 20.8 P P 0.920 0.8940.871 0.944 0.901 0.951 P 1782 26.3 25.0 30.0 21.2 P P 0.001 0.003 0.4920.443 0.376 0.255 P 2774321 24.8 26.2 31.1 19.8 P P 0.887 0.005 0.5090.004 0.435 0.025 P 2512541 21.6 23.6 27.8 18.1 P P 0.612 0.089 0.5710.029 0.723 0.135 P 2109 23.1 25.3 28.1 21.4 P P 0.514 0.728 0.934 0.3990.679 0.893 P 2005 22.1 24.2 27.8 21.7 P P 0.543 0.518 0.768 0.803 0.9890.899 P 1145 22.8 25.6 31.0 19.3 P P 0.168 0.088 0.097 0.005 0.275 0.015P 2545687 19.7 21.6 28.4 19.0 P P 0.748 0.710 0.061 0.869 0.064 0.161 P1824 26.1 25.2 30.0 21.0 P P 0.004 0.003 0.539 0.266 0.443 0.217 P 55422.3 22.6 25.8 19.1 P P 0.150 0.131 0.405 0.572 0.860 0.864 P 215239925.2 25.0 31.4 20.1 P P 0.047 0.003 0.573 0.083 0.111 0.025 P 1630 23.924.9 26.2 23.0 P P 0.499 0.756 0.146 0.412 0.221 0.150 P 2089 22.5 24.328.3 20.3 P P 0.812 0.467 0.732 0.343 0.803 0.468 P 1914 21.0 22.0 25.518.5 P P 0.519 0.346 0.774 0.591 0.990 0.758 P 1189 20.9 24.5 28.0 20.3P P 0.024 0.613 0.289 0.275 0.989 0.538 P 2677542 25.1 26.7 30.1 22.9 PP 0.989 0.472 0.952 0.438 0.943 0.710 P 2073538 23.5 23.5 29.5 20.4 P P0.074 0.152 0.656 0.814 0.171 0.218 P 1967 21.6 23.1 26.2 19.5 P P 0.9050.533 0.810 0.563 0.842 0.873 P 2550576 22.5 24.2 27.2 22.5 P P 0.9110.372 0.810 0.384 0.754 0.462 P 438 22.7 22.3 26.9 18.0 P P 0.024 0.0080.659 0.241 0.514 0.240 P 1550 25.1 26.0 30.1 23.2 P P 0.420 0.635 0.9700.938 0.718 0.804 P 2087 21.4 23.4 26.7 19.4 P P 0.655 0.538 0.941 0.3270.886 0.668 P 1902 22.4 24.4 26.5 21.4 P P 0.591 0.820 0.619 0.884 0.4230.573 P 3485126 24.1 27.1 29.8 23.9 P P 0.108 0.421 0.720 0.761 0.6780.868 P 1957 23.8 24.5 28.6 20.7 P P 0.306 0.138 0.848 0.400 0.761 0.477P 278 24.3 25.4 31.7 21.3 P P 0.528 0.155 0.247 0.289 0.120 0.066 P2276373 23.6 24.2 28.4 21.0 P P 0.312 0.297 0.904 0.705 0.708 0.609 P2318175 21.7 23.9 28.5 21.0 P P 0.485 0.699 0.369 0.925 0.538 0.584 P2079564 23.4 24.3 27.6 18.0 P P 0.426 0.001 0.647 0.005 0.925 0.136 P2295511 26.3 26.1 29.3 22.7 P P 0.041 0.063 0.274 0.612 0.875 0.877 P2495 23.5 25.5 27.0 21.3 P P 0.575 0.466 0.420 0.237 0.252 0.791 P 183725.2 26.9 32.1 23.3 P P 0.924 0.634 0.346 0.564 0.335 0.273 P 1223 24.826.3 28.9 23.1 P P 0.934 0.719 0.575 0.747 0.576 0.786 P 2193 25.1 26.431.2 23.4 P P 0.762 0.700 0.593 0.849 0.468 0.490 P 2045301 25.0 25.331.1 21.0 P P 0.176 0.041 0.570 0.229 0.199 0.089 P 2469217 21.2 22.527.3 19.9 P P 0.681 0.933 0.630 0.835 0.470 0.641 P 2351284 20.9 21.922.4 19.2 P P 0.466 0.733 0.057 0.870 0.094 0.150 P 2712378 21.6 23.226.5 19.3 P P 0.949 0.415 0.921 0.411 0.941 0.692 P 2739656 24.6 26.229.0 23.6 P P 0.984 0.856 0.704 0.858 0.676 0.664 P 2462 20.0 24.1 27.721.5 P P 0.006 0.041 0.186 0.834 0.953 0.945 P 1835 22.1 23.5 26.6 21.1P P 0.848 0.837 0.767 0.720 0.824 0.705 P 1400 22.0 23.9 25.0 20.7 P P0.694 0.990 0.286 0.766 0.180 0.361 P 2662486 24.7 24.7 27.6 22.1 P P0.071 0.279 0.247 0.879 0.732 0.719 P 2654139 20.8 22.4 24.0 20.1 P P0.905 0.679 0.339 0.723 0.278 0.284 P 2309104 23.0 25.8 28.6 20.0 P P0.192 0.182 0.797 0.018 0.709 0.312 P 1426 23.4 24.1 28.9 21.7 P P 0.3290.767 0.810 0.696 0.457 0.702 P 2661366 26.4 25.9 29.9 22.2 P P 0.0180.022 0.410 0.466 0.765 0.519 P 2143801 25.0 25.6 28.9 22.6 P P 0.2550.363 0.542 0.884 0.931 0.991 P 1717 23.3 24.7 28.5 20.9 P P 0.849 0.3800.943 0.424 0.864 0.561 P 1863 23.7 25.6 26.9 22.4 P P 0.771 0.970 0.3100.801 0.216 0.392 P 1218 24.9 26.4 29.3 23.1 P P 0.870 0.622 0.689 0.6830.728 0.957 P 2566446 21.9 22.0 25.5 20.0 P P 0.080 0.589 0.431 0.4900.979 0.718 P 2355070 23.3 26.3 30.2 20.0 P P 0.105 0.111 0.325 0.0040.803 0.073 P 2512233 24.8 26.2 31.2 20.6 P P 0.808 0.023 0.495 0.0240.393 0.053 P 2317 22.1 25.1 25.8 21.9 P P 0.111 0.428 0.489 0.757 0.1240.287 P 2286 22.9 25.7 28.6 22.3 P P 0.179 0.636 0.761 0.639 0.731 0.986P 1128 21.6 25.0 27.7 22.3 P P 0.037 0.131 0.605 0.923 0.626 0.656 P1536 19.4 23.4 24.9 17.3 P P 0.008 0.492 0.840 0.008 0.266 0.561 P2257043 21.1 22.5 27.0 17.7 P P 0.786 0.098 0.660 0.117 0.544 0.174 P2231993 22.7 23.1 25.9 20.1 P P 0.190 0.287 0.319 0.852 0.677 0.817 P1521 25.5 24.8 27.5 22.3 P P 0.011 0.142 0.111 0.785 0.657 0.611 P 22024.1 24.8 27.5 20.0 P P 0.338 0.027 0.360 0.097 0.614 0.606 P 893 23.824.8 28.5 20.8 P P 0.490 0.177 0.826 0.348 0.913 0.543 P 2138 23.6 25.530.3 23.0 P P 0.705 0.595 0.389 0.770 0.462 0.667 P 2076 22.3 24.3 25.822.6 P P 0.660 0.250 0.400 0.364 0.261 0.159 P 1142 23.1 26.8 28.7 24.6P P 0.016 0.036 0.794 0.632 0.355 0.312 P 1539 22.8 24.3 30.3 20.8 P P0.923 0.552 0.211 0.572 0.165 0.152 P 349 21.9 23.6 26.2 20.1 P P 0.8870.652 0.672 0.560 0.599 0.921 P 1669 26.0 25.8 28.1 23.6 P P 0.042 0.3670.116 0.610 0.501 0.410 P 2740095 21.3 24.7 28.6 22.1 P P 0.050 0.1190.269 0.805 0.833 0.974 P 2855706 21.2 22.8 26.8 19.9 P P 0.962 0.9820.793 0.954 0.796 0.810 P 2259 20.6 22.1 27.0 16.6 P P 0.908 0.032 0.5020.027 0.436 0.064 P 2593636 22.0 22.6 28.3 17.2 P P 0.230 0.006 0.5310.042 0.204 0.032 P 2937810 22.8 24.3 30.0 20.8 P P 0.862 0.540 0.2700.597 0.204 0.189 P 2792222 24.2 25.4 29.2 23.2 P P 0.656 0.846 0.9310.596 0.896 0.850 P 1853 22.2 23.3 26.3 19.6 P P 0.582 0.312 0.639 0.4960.818 0.847 P 2802981 22.6 25.2 28.1 19.1 P P 0.225 0.092 0.797 0.0080.743 0.223 P 2060742 21.4 22.3 25.5 18.8 P P 0.406 0.277 0.584 0.5770.863 0.865 P 820 20.8 21.2 25.5 17.8 P P 0.213 0.181 0.866 0.599 0.6600.518 P 922 21.7 22.1 28.0 18.7 P P 0.180 0.158 0.556 0.592 0.194 0.179P 2319 25.9 25.8 34.3 21.2 P P 0.059 0.008 0.088 0.143 0.006 0.002 P1532 23.9 25.9 30.4 24.1 P P 0.659 0.300 0.474 0.432 0.583 0.995 P 63224.4 25.4 31.0 23.7 P P 0.483 0.734 0.438 0.384 0.238 0.640 P 2226 24.125.1 29.6 22.4 P P 0.507 0.721 0.830 0.920 0.575 0.692 P 2332187 21.523.7 26.8 18.3 P P 0.497 0.134 0.915 0.037 0.822 0.328 P 2423724 23.726.1 29.6 20.9 P P 0.391 0.222 0.692 0.055 0.998 0.287 P 2269 25.5 26.529.1 23.1 P P 0.574 0.381 0.455 0.598 0.602 0.899 P 893_1 24.8 26.3 28.521.0 P P 0.878 0.045 0.455 0.043 0.468 0.589 P 1016 24.8 24.9 26.8 21.4P P 0.113 0.106 0.107 0.565 0.347 0.662 P 2914265 24.4 25.8 28.7 21.1 PP 0.912 0.130 0.687 0.125 0.705 0.585 P 2235173 23.6 24.5 31.8 19.8 P P0.454 0.055 0.098 0.133 0.032 0.010 P 615 24.3 25.3 30.3 23.8 P P 0.5250.592 0.629 0.302 0.404 0.920 P 2555715 24.5 26.5 30.7 21.5 P P 0.6640.173 0.557 0.077 0.679 0.189 P 3032 25.0 26.3 31.2 21.1 P P 0.725 0.0360.561 0.048 0.424 0.082 P 2533160 23.9 26.6 31.9 24.4 P P 0.203 0.1810.138 0.614 0.338 0.623 P 2108553 25.0 25.6 30.4 21.1 P P 0.280 0.0390.889 0.157 0.491 0.181 P 2855460 24.3 25.6 26.6 21.5 P P 0.718 0.2230.149 0.299 0.171 0.598 P 2505890 25.3 26.9 30.1 21.5 P P 0.945 0.0460.851 0.037 0.867 0.308 P 438_1 24.9 24.5 28.7 21.4 P P 0.028 0.0890.522 0.820 0.685 0.651 P 1670 24.1 24.6 29.1 21.7 P P 0.196 0.344 0.9390.941 0.574 0.622 P 2895821 26.2 26.8 30.2 23.4 P P 0.271 0.241 0.5630.651 0.942 0.847 P 382 24.8 24.3 27.6 22.3 P P 0.020 0.322 0.222 0.5330.878 0.639 P 923 23.4 24.2 26.0 19.6 P P 0.406 0.051 0.188 0.138 0.3180.986 P 739 20.6 21.3 25.1 17.2 P P 0.319 0.109 0.775 0.324 0.850 0.484P 1023 25.4 25.7 29.0 21.7 P P 0.127 0.058 0.431 0.358 0.932 0.659 P2197135 26.2 26.7 33.0 22.2 P P 0.234 0.033 0.365 0.160 0.117 0.041 P2442 23.8 24.7 29.8 20.3 P P 0.421 0.075 0.645 0.187 0.370 0.147 P 324523.0 25.3 29.1 21.8 P P 0.486 0.967 0.607 0.584 0.838 0.640 P 221461822.5 23.5 27.9 20.1 P P 0.502 0.375 0.865 0.645 0.605 0.502 P 292310425.6 28.3 31.1 24.1 P P 0.216 0.829 0.836 0.260 0.693 0.759 P 257718525.3 25.9 26.8 23.8 P P 0.229 0.839 0.058 0.513 0.152 0.130 P 269174125.6 25.9 29.1 23.6 P P 0.147 0.583 0.394 0.642 0.848 0.681 P 2067 26.127.6 29.9 23.7 P P 0.940 0.360 0.497 0.357 0.490 0.964 P 2866838 24.627.4 29.7 24.1 P P 0.193 0.592 0.993 0.711 0.510 0.746 P 651 24.2 25.825.2 19.1 P P 0.979 0.003 0.033 0.002 0.023 0.935 P 784 22.8 24.4 27.821.8 P P 0.991 0.854 0.966 0.851 0.959 0.884 P 2538 22.5 22.5 24.8 20.5P P 0.072 0.530 0.135 0.526 0.479 0.357 P 243 22.6 22.8 25.3 18.1 P P0.134 0.014 0.210 0.126 0.551 0.713 P 2024940 23.7 26.4 30.3 25.1 P P0.195 0.043 0.428 0.222 0.839 0.608 P 2473497 25.3 26.5 29.8 21.3 P P0.657 0.037 0.776 0.058 0.934 0.325 P 2727912 24.6 25.6 27.0 21.1 P P0.569 0.093 0.161 0.168 0.222 0.828 P 687 24.3 25.5 31.0 20.4 P P 0.6620.039 0.400 0.059 0.262 0.051 P 160 24.3 24.8 29.0 21.2 P P 0.245 0.1670.858 0.527 0.698 0.507 P 670 27.3 27.5 29.9 24.9 P P 0.109 0.394 0.2000.802 0.565 0.547 P 2629101 26.5 26.9 30.3 24.4 P P 0.186 0.526 0.5130.778 0.967 0.850 P 2532 23.1 24.3 28.2 17.7 P P 0.706 0.001 0.999 0.0020.852 0.060 P 2071896 24.0 25.9 29.8 20.8 P P 0.713 0.132 0.725 0.0610.846 0.233 P 2788747 25.9 27.6 29.8 22.6 P P 0.903 0.108 0.550 0.0720.482 0.665 P 1611 22.8 24.3 27.6 17.9 P P 0.919 0.005 0.904 0.004 0.9370.121 P 1248 21.8 23.1 28.7 18.9 P P 0.738 0.215 0.346 0.280 0.239 0.122P 790 23.6 23.7 27.3 19.4 P P 0.091 0.021 0.454 0.216 0.969 0.474 P 107924.3 25.2 30.3 24.0 P P 0.483 0.438 0.624 0.182 0.381 0.972 P 3063 24.725.9 29.9 20.2 P P 0.651 0.013 0.947 0.020 0.767 0.127 P 3117 25.0 25.329.7 20.8 P P 0.143 0.024 0.865 0.176 0.583 0.234 P 852 25.5 26.2 29.422.6 P P 0.351 0.223 0.527 0.532 0.831 0.862 P 2295 23.8 23.8 25.1 20.4P P 0.072 0.096 0.046 0.637 0.214 0.460 P 2912 25.1 26.1 31.0 20.9 P P0.488 0.022 0.672 0.053 0.423 0.086 P 3137 24.9 25.8 29.1 22.5 P P 0.3980.359 0.621 0.715 0.914 0.908 P 1206 22.0 22.5 25.0 20.1 P P 0.200 0.5810.271 0.734 0.589 0.533 P 2974887 23.0 24.6 29.5 20.8 P P 0.972 0.4640.446 0.450 0.404 0.275 P 2526 23.1 25.3 30.7 21.6 P P 0.495 0.809 0.1900.452 0.286 0.202 P 3044 22.0 22.8 27.0 20.1 P P 0.336 0.586 0.960 0.9070.669 0.780 P 2907847 22.8 23.6 26.6 19.1 P P 0.337 0.060 0.476 0.1910.775 0.620 P 3210 23.3 25.0 26.2 22.1 P P 0.913 0.994 0.235 0.943 0.1840.303 P 3199 25.4 25.1 30.0 21.4 P P 0.033 0.039 0.801 0.508 0.429 0.316P 3318 26.5 27.4 32.4 22.5 P P 0.431 0.037 0.659 0.098 0.386 0.106 P3111 24.5 26.1 29.1 21.3 P P 0.990 0.140 0.803 0.113 0.784 0.511 P3210_1 26.4 26.7 29.5 23.2 P P 0.160 0.129 0.310 0.547 0.698 0.983 P3088 24.0 24.8 26.9 20.0 P P 0.398 0.037 0.246 0.107 0.410 0.818 P2912876 27.2 28.4 32.7 26.0 P P 0.635 0.995 0.831 0.726 0.642 0.857 P2825793 25.6 25.4 30.2 22.1 P P 0.044 0.083 0.769 0.695 0.491 0.440 P2761 20.7 21.2 25.9 17.4 P P 0.233 0.127 0.936 0.444 0.496 0.331 P 244022.1 22.4 26.1 20.0 P P 0.128 0.473 0.542 0.737 0.917 0.919 P

Table 8 shows raw data results for 2 samples (of 546) that failed boththe Normal controls and Relative controls method.

TABLE 8 Relative Controls Normal Controls CEACAM4/ CEACAM4/ SampleCEACAM4 LAMP1 PLA2G7 PLAC8 Status LAMP1 pVal PLAC8 pVal 2712753 21.3022.23 36.06 18.67 F 0.460 0.286 2023537 25.29 25.22 37.02 21.26 F 0.0620.032 Relative Controls CEACAM4/ LAMP1/ LAMP1/ PLAC8/ Sample PLA2G7 pValPLAC8 pVal PLA2G7 pVal PLA2G7 pVal Status 2712753 0.000 0.547 0.0000.000 F 2023537 0.001 0.351 0.000 0.000 F

Table 9 shows raw data results for 26 samples (of 546) that failed boththe Normal controls but passed the Relative controls method.

TABLE 9 Normal Controls Relative Controls External CEACAM4/ CEACAM4/Sample CEACAM4 LAMP1 PLA2G7 PLAC8 Control Status LAMP1 pVal PLAC8 pVal6759 22.50 23.66 26.51 22.02 F F 0.629 0.566 6769 23.89 24.05 27.3221.34 F F 0.107 0.317 6789 22.52 23.26 26.83 21.76 F F 0.338 0.717 681221.97 23.71 26.12 22.12 F F 0.864 0.290 6829 22.04 23.95 26.75 22.49 F F0.721 0.197 6834 21.29 23.97 29.11 20.88 F F 0.221 0.526 6836 21.2023.52 27.88 20.38 F F 0.412 0.753 6843 21.42 22.58 24.07 20.46 F F 0.6320.833 6852 21.90 24.03 26.33 22.64 F F 0.546 0.132 6853 23.87 24.4925.97 21.25 F F 0.273 0.289 6870 24.10 25.35 28.17 22.11 F F 0.702 0.564IXP-109 22.61 24.05 27.79 20.50 F F 0.866 0.503 ixp-136 23.04 24.4125.56 19.59 F F 0.808 0.090 IXP-138 23.47 24.12 28.75 20.79 F F 0.2910.272 IXP-139 22.12 23.35 26.09 20.97 F F 0.688 0.947 IXP-140 21.4823.36 27.55 22.04 F F 0.744 0.168 IXP-141 23.95 25.12 27.55 20.63 F F0.637 0.111 IXP-143 23.24 24.40 26.09 22.16 F F 0.635 0.903 IXP-14422.52 24.89 27.06 22.01 F F 0.378 0.579 IXP-146 22.05 24.19 27.20 21.35F F 0.531 0.682 IXP-147 24.99 25.72 26.24 21.69 F F 0.332 0.115 IXP-14821.46 23.18 25.75 21.80 F F 0.887 0.228 IXP-149 23.67 24.19 25.50 22.14F F 0.226 0.821 6869 31.60 35.99 37.22 33.46 P F 0.002 0.018 IXP_12831.87 33.39 38.03 28.11 P F 0.940 0.053 1357 27.27 27.05 35.06 22.12 P F0.042 0.003 Relative Controls CEACAM4/ LAMP1/ LAMP1/ PLAC8/ SamplePLA2G7 pVal PLAC8 pVal PLA2G7 pVal PLA2G7 pVal Status 6759 0.570 0.3370.712 0.407 P 6769 0.385 0.921 0.897 0.873 P 6789 0.681 0.280 0.9710.569 P 6812 0.622 0.315 0.539 0.293 P 6829 0.842 0.264 0.695 0.350 P6834 0.153 0.834 0.356 0.391 P 6836 0.405 0.796 0.627 0.594 P 6843 0.2000.568 0.256 0.218 P 6852 0.726 0.242 0.498 0.233 P 6853 0.117 0.7360.256 0.467 P 6870 0.593 0.735 0.702 0.904 P IXP-109 0.961 0.554 0.8910.661 P ixp-136 0.178 0.103 0.185 0.871 P IXP-138 0.920 0.683 0.5260.461 P IXP-139 0.555 0.718 0.665 0.583 P IXP-140 0.607 0.218 0.6970.711 P IXP-141 0.434 0.174 0.546 0.789 P IXP-143 0.240 0.635 0.3060.277 P IXP-144 0.774 0.963 0.455 0.564 P IXP-146 0.974 0.986 0.7810.830 P IXP-147 0.044 0.329 0.094 0.420 P IXP-148 0.674 0.237 0.6020.282 P IXP-149 0.087 0.526 0.218 0.179 P 6869 0.784 0.809 0.201 0.245 PIXP_128 0.578 0.045 0.526 0.105 P 1357 0.157 0.085 0.011 0.003 P

Table 10 shows raw data results for 13 samples (of 546) that passed theNormal controls but failed the Relative controls method.

TABLE 10 Normal Controls Relative Controls External CEACAM4/ CEACAM4/Sample CEACAM4 LAMP1 PLA2G7 PLAC8 Control Status LAMP1 pVal PLAC8 pVal2400065 21.11 27.01 29.58 23.37 P P 0.000 0.008 2491930 21.48 26.4228.04 23.01 P P 0.000 0.035 2283614 22.05 26.95 30.13 24.69 P P 0.0000.003 2541845 20.28 25.17 27.53 22.41 P P 0.000 0.010 3787 26.15 30.7331.07 21.69 P P 0.001 0.013 2781056 22.21 27.49 29.08 24.38 P P 0.0000.009 2636948 19.62 24.71 27.18 21.20 P P 0.000 0.031 2580739 27.2726.55 29.78 20.66 P P 0.009 0.000 1329 26.14 24.03 30.86 22.36 P P 0.0000.052 2423113 23.90 26.72 32.41 19.80 P P 0.164 0.029 2791 25.57 23.5931.40 19.13 P P 0.000 0.000 1914 24.65 26.29 29.91 17.72 P P 0.958 0.0002452 23.48 22.98 26.30 17.56 P P 0.018 0.000 Relative Controls CEACAM4/LAMP1/ LAMP1/ PLAC8/ Sample PLA2G7 pVal PLAC8 pVal PLA2G7 pVal PLA2G7pVal Status 2400065 0.077 0.508 0.600 0.956 F 2491930 0.443 0.628 0.2870.557 F 2283614 0.118 0.651 0.852 0.688 F 2541845 0.259 0.956 0.5210.585 F 3787 0.929 0.000 0.076 0.166 F 2781056 0.352 0.818 0.283 0.461 F2636948 0.196 0.574 0.563 0.877 F 2580739 0.177 0.012 0.880 0.206 F 13290.847 0.346 0.061 0.326 F 2423113 0.073 0.001 0.219 0.004 F 2791 0.7010.180 0.016 0.007 F 1914 0.933 0.000 0.948 0.008 F 2452 0.234 0.0350.920 0.276 F

Throughout this specification and claims which follow, unless thecontext requires otherwise, the word “comprise”, and variations such as“comprises” or “comprising”, will be understood to imply the inclusionof a stated integer or group of integers or steps but not the exclusionof any other integer or group of integers.

Persons skilled in the art will appreciate that numerous variations andmodifications will become apparent. All such variations andmodifications which become apparent to persons skilled in the art,should be considered to fall within the spirit and scope that theinvention broadly appearing before described.

1.-47. (canceled)
 48. A method for validating quantification ofbiomarkers, the biomarkers being quantified using a quantificationtechnique of a selected type, and the method including: a) determining aplurality of biomarker values, each biomarker value being indicative ofa value measured or derived from a measured value, for at least onecorresponding biomarker of the biological subject and being at leastpartially indicative of a concentration of the biomarker in a sampletaken from the subject; b) determining control values by determiningcombinations of biomarker values; c) comparing each control value to arespective control reference; and, d) determining if the biomarkervalues are valid using results of the comparison.
 49. A method accordingto claim 48, wherein at least first and second biomarker values are usedto determine an indicator indicative of a test result, and wherein themethod includes determining control values including: a) a combinationof the first and at least one other biomarker value; and, b) acombination of the second and at least one other biomarker value.
 50. Amethod according to claim 49, wherein the method includes: a)determining at least four biomarker values, the indicator being based ona combination of: i) a first indicator value calculated using first andsecond biomarker values; and, ii) a second indicator value calculatedusing third and fourth biomarker values; and, b) determining controlvalues including: i) a first control value calculated using first andthird biomarker values; ii) a second control value calculated usingfirst and fourth biomarker values; iii) a third control value calculatedusing second and third biomarker values; and, iv) a fourth control valuecalculated using second and fourth biomarker values.
 51. A methodaccording to claim 50, wherein the method includes calculating at leastone of the indicator values and the control values by applying afunction to the respective biomarker values wherein the functionincludes at least one of: a) multiplying two biomarker values; b)dividing two biomarker values; c) a ratio of two biomarker values; d)adding two biomarker values; e) subtracting two biomarker values; f) aweighted sum of at least two biomarker values; g) a log sum of at leasttwo biomarker values; and, h) a sigmoidal function of at least twobiomarker values.
 52. A method according to claim 48, wherein the methodincludes determining: a) a first control value using a ratio of firstand third biomarker values; b) a second control value using a ratio offirst and fourth biomarker values; c) a third control value using aratio of second and third biomarker values; and, d) a fourth controlvalue using a ratio of second and fourth biomarker values.
 53. A methodaccording to claim 48, wherein the method includes: a) determining avalidity probability based on the result of the comparison; and, b)using the validity probability to determine if the biomarker values arevalid.
 54. A method according to claim 53, wherein the method includes:a) determining a control value probability for the comparison of eachcontrol value to the respective control reference; and, b) combining thecontrol value probabilities to determine the validity probability.
 55. Amethod according to claim 54, wherein the control reference is derivedfrom biomarker values collected from a number of individuals in a samplepopulation and is at least one of: a) a control value threshold range;b) a control value threshold; and, c) a control value distribution. 56.A method according to claim 55, wherein the control reference is acontrol value threshold range, and wherein the method includes: a)comparing each control value to a respective control value thresholdrange; and, b) determining at least one of the biomarker values to beinvalid if any one of the control values falls outside the respectivecontrol value threshold range.
 57. A method according to claim 55,wherein the control reference is a control value distribution, andwherein the method includes: a) comparing each control value to arespective control value distribution; and, b) determining the validityusing the results of the comparisons.
 58. A method according to claim49, wherein the indicator is for use in determining the likelihood thata biological subject has at least one medical condition, the controlreference is derived from biomarker values collected from a number ofindividuals in a sample population and the sample population includes:a) individuals presenting with clinical signs of the at least onemedical condition; b) individuals diagnosed with the at least onemedical condition; and, c) healthy individuals.
 59. A method accordingto claim 49, wherein the indicator is determined by combining the firstand second derived indicator values using a combining function, thecombining function being at least one of: a) an additive model; b) alinear model; c) a support vector machine; d) a neural network model; e)a random forest model; f) a regression model; g) a genetic algorithm; h)an annealing algorithm; i) a weighted sum; and, j) A nearest neighbourmodel.
 60. A method according to claim 49, wherein the method includes:a) determining an indicator value indicative of a likelihood of thesubject having at least one medical condition; b) comparing theindicator value to at least one indicator value range; c) determiningthe indicator at least in part using a result of the comparison; and, d)generating a representation of the indicator.
 61. A method according toclaim 48, wherein the biomarker value is indicative of a level orabundance of a molecule, cell or organism selected from one or more of:a) proteins; b) nucleic acids; c) carbohydrates; d) lipids; e)proteoglycans; f) cells; g) metabolites; h) tissue sections; i) wholeorganisms; and, j) molecular complexes.
 62. A method according to claim48, wherein the method is performed at least in part using one or moreelectronic processing devices wherein the method includes, in the one ormore electronic processing devices: a) receiving the biomarker values;b) determining the at least one control value using at least two of thebiomarker values; c) retrieving the indicator reference from a database;d) comparing the at least one control value to the respective controlvalue threshold; and, e) determining if the test is a valid test usingthe results of the comparison.
 63. A method according to claim 48,wherein the biomarkers are gene expression products and wherein themethod includes: a) obtaining a sample from a biological subject, thesample including the gene expression products; b) amplifying at leastthe gene expression products in the sample; and, c) for each geneexpression product, determining an amplification amount representing adegree of amplification required to obtain a defined level of therespective gene expression.
 64. A method according to claim 63, whereinthe amplification amount is at least one of: a) a cycle time; b) anumber of cycles; c) a cycle threshold; and, d) an amplification time.65. A method according to claim 63, wherein the biomarkers are geneexpression products and wherein the method includes, determining acombination of biomarker values by subtracting amplification amounts forthe respective gene expression products so that the combination ofbiomarker values represents a ratio of the relative concentration of therespective gene expression products.
 66. A method according to claim 48,wherein the biomarker values are obtained from a biological subjectpresenting with clinical signs of at least one medical condition whereinthe at least one condition includes ipSIRS and wherein the biomarkervalues correspond to relative concentrations of LAMP1, CEACAM4, PLAC8and PLA2G7.
 67. A method according to claim 66, wherein the biomarkervalues are obtained from a biological subject presenting with clinicalsigns common to first and second conditions and wherein the indicator isfor use in distinguishing between the first and second conditions andwherein the first and second conditions include inSIRS and ipSIRS.
 68. Amethod according to claim 48, wherein the quantification technique is atleast one of: a) a nucleic acid amplification technique; b) polymerasechain reaction (PCR); c) a hybridisation technique; d) microarrayanalysis; e) low density arrays; f) hybridisation with allele-specificprobes; g) enzymatic mutation detection; h) ligation chain reaction(LCR); i) oligonucleotide ligation assay (OLA); j) flow-cytometricheteroduplex analysis; k) chemical cleavage of mismatches; l) massspectrometry; m) flow cytometry; n) liquid chromatography; o) gaschromatography; p) immunohistochemistry; q) nucleic acid sequencing; r)single strand conformation polymorphism (SSCP); s) denaturing gradientgel electrophoresis (DGGE); t) temperature gradient gel electrophoresis(TGGE); u) restriction fragment polymorphisms; v) serial analysis ofgene expression (SAGE); w) affinity assays; x) radioimmunoassay (RIA);y) lateral flow immunochromatography; z) flow cytometry; aa) electronmicroscopy (EM); and, bb) enzyme-substrate assay.
 69. Apparatus forvalidating measurement of biomarker values used in generating anindicator, the biomarkers being quantified using a quantificationtechnique of a selected type, and the apparatus including at least oneprocessing device that: a) determines a plurality of biomarker values,each biomarker value being indicative of a value measured or derivedfrom a measured value, for at least one corresponding biomarker of thebiological subject and being at least partially indicative of aconcentration of the biomarker in a sample taken from the subject; b)determines control values by determining different combinations ofbiomarker values; c) compares each control value to a respective controlreference; and, d) determines if the biomarker values are valid usingresults of the comparison.
 70. A method according to claim 48, whereinthe biomarker values are used to determine an indicator indicative of atest result, and wherein the combinations include ratios of thebiomarker values.